modelId,author,paper_refs
bert-base-uncased,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
xlm-roberta-base,huggingface,"['arxiv.org/abs/1911.02116', 'bibtex']"
openai/clip-vit-large-patch14,openai,['arxiv.org/abs/2103.00020']
roberta-base,huggingface,"['arxiv.org/abs/1907.11692', 'bibtex']"
gpt2,huggingface,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
roberta-large,huggingface,"['arxiv.org/abs/1907.11692', 'bibtex']"
distilbert-base-uncased,huggingface,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
bert-base-cased,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
microsoft/deberta-base,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
google/vit-base-patch16-224,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
openai/clip-vit-base-patch32,openai,['arxiv.org/abs/2103.00020']
albert-base-v2,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
facebook/bart-base,facebook,"['arxiv.org/abs/1910.13461', 'bibtex']"
sentence-transformers/paraphrase-MiniLM-L6-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
bert-base-multilingual-cased,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
t5-small,huggingface,"['arxiv.org/abs/1805.12471', 'bibtex']"
ProsusAI/finbert,ProsusAI,['arxiv.org/abs/1908.10063']
distilgpt2,huggingface,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
facebook/contriever-msmarco,facebook,['arxiv.org/abs/2112.09118']
t5-base,huggingface,"['arxiv.org/abs/1805.12471', 'bibtex']"
facebook/bart-large-mnli,facebook,['arxiv.org/abs/1910.13461']
Hate-speech-CNERG/indic-abusive-allInOne-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
tals/albert-xlarge-vitaminc-mnli,tals,['bibtex']
google/bert_uncased_L-12_H-768_A-12,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
xlm-roberta-large,huggingface,"['arxiv.org/abs/1911.02116', 'bibtex']"
EleutherAI/gpt-j-6B,EleutherAI,['arxiv.org/abs/2104.09864']
distilroberta-base,huggingface,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
bert-large-uncased,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
facebook/bart-large-cnn,facebook,"['arxiv.org/abs/1910.13461', 'bibtex']"
sentence-transformers/distiluse-base-multilingual-cased-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
distilbert-base-multilingual-cased,huggingface,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
flair/pos-english-fast,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
bert-base-multilingual-uncased,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
facebook/mbart-large-50,facebook,"['arxiv.org/abs/2008.00401', {'title': 'Multilingual Translation with Extensible Multilingual Pretraining and Finetuning'}]"
distilbert-base-cased-distilled-squad,huggingface,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
EleutherAI/gpt-neo-1.3B,EleutherAI,"['doi.org/10.5281/zenodo.5297715}', {'title': 'The Pile: An 800GB Dataset of Diverse Text for Language Modeling'}]"
dslim/bert-large-NER,dslim,"['arxiv.org/abs/1810.04805', 'bibtex']"
prajjwal1/bert-tiny,prajjwal1,"['arxiv.org/abs/1908.08962', 'bibtex']"
gpt2-medium,huggingface,"['arxiv.org/abs/1910.09700', {'title': 'Language models are unsupervised multitask learners'}]"
sentence-transformers/bert-base-nli-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
neuralmind/bert-base-portuguese-cased,neuralmind,['bibtex']
google/vit-base-patch16-224-in21k,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
microsoft/layoutlmv2-base-uncased,microsoft,['arxiv.org/abs/2012.14740']
jplu/tf-xlm-r-ner-40-lang,jplu,['arxiv.org/abs/1911.02116']
sentence-transformers/all-mpnet-base-v2,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
klue/bert-base,klue,['arxiv.org/abs/2105.09680']
pyannote/segmentation,pyannote,"['arxiv.org/abs/2104.04045', 'bibtex']"
sentence-transformers/paraphrase-mpnet-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/all-MiniLM-L12-v2,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
bert-large-cased,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
cardiffnlp/twitter-roberta-base-sentiment-latest,cardiffnlp,['arxiv.org/abs/2202.03829']
microsoft/deberta-v3-base,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
emilyalsentzer/Bio_ClinicalBERT,emilyalsentzer,['arxiv.org/abs/1904.03323']
facebook/bart-large,facebook,"['arxiv.org/abs/1910.13461', 'bibtex']"
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
finiteautomata/bertweet-base-sentiment-analysis,finiteautomata,['arxiv.org/abs/2106.09462']
google/bert_uncased_L-2_H-128_A-2,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
microsoft/layoutxlm-base,microsoft,['arxiv.org/abs/2104.08836']
joeddav/xlm-roberta-large-xnli,joeddav,['arxiv.org/abs/1911.02116']
hfl/chinese-bert-wwm-ext,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
mrm8488/t5-base-finetuned-common_gen,mrm8488,['arxiv.org/abs/1911.03705']
camembert-base,huggingface,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
hfl/chinese-roberta-wwm-ext,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco,sebastian-hofstaetter,"['arxiv.org/abs/2104.06967', 'bibtex']"
EleutherAI/gpt-neo-125M,EleutherAI,"['doi.org/10.5281/zenodo.5297715}', {'title': 'The Pile: An 800GB Dataset of Diverse Text for Language Modeling'}]"
distilbert-base-cased,huggingface,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
google/bert_uncased_L-6_H-768_A-12,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
bert-large-uncased-whole-word-masking-finetuned-squad,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
google/bigbird-roberta-base,google,['arxiv.org/abs/2007.14062']
finiteautomata/beto-sentiment-analysis,finiteautomata,['arxiv.org/abs/2106.09462']
dslim/bert-base-NER,dslim,"['arxiv.org/abs/1810.04805', 'bibtex']"
microsoft/deberta-v3-large,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
xlm-roberta-large-finetuned-conll03-english,huggingface,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
t5-large,huggingface,"['arxiv.org/abs/1805.12471', 'bibtex']"
emilyalsentzer/Bio_Discharge_Summary_BERT,emilyalsentzer,['arxiv.org/abs/1904.03323']
facebook/m2m100_418M,facebook,['arxiv.org/abs/2010.11125']
pyannote/speaker-diarization,pyannote,"['arxiv.org/abs/2012.01477', 'doi.org/10.1016/j.csl.2021.101254)):', 'bibtex']"
uer/albert-base-chinese-cluecorpussmall,uer,[{'title': 'Albert: A lite bert for self-supervised learning of language representations'}]
TurkuNLP/bert-base-finnish-cased-v1,TurkuNLP,['arxiv.org/abs/1912.07076']
facebook/opt-125m,facebook,['arxiv.org/abs/2205.01068']
facebook/wav2vec2-base-960h,facebook,['arxiv.org/abs/2006.11477']
bhadresh-savani/distilbert-base-uncased-emotion,bhadresh-savani,['arxiv.org/abs/1910.01108']
uer/gpt2-chinese-cluecorpussmall,uer,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
Rostlab/prot_bert,Rostlab,['doi.org/10.1101/2020.07.12.199554)']
siebert/sentiment-roberta-large-english,siebert,[{'title': 'More than a feeling: Accuracy and Application of Sentiment Analysis'}]
xlnet-base-cased,huggingface,"['arxiv.org/abs/1906.08237', 'bibtex']"
dccuchile/bert-base-spanish-wwm-uncased,dccuchile,"['arxiv.org/abs/1812.10464', {'title': 'Spanish Pre-Trained BERT Model and Evaluation Data'}]"
pyannote/embedding,pyannote,['bibtex']
google/t5-v1_1-base,google,['arxiv.org/abs/2002.05202']
gpt2-xl,huggingface,"['arxiv.org/abs/1910.09700', {'title': 'Language models are unsupervised multitask learners'}]"
valhalla/t5-base-e2e-qg,valhalla,['arxiv.org/abs/1910.10683']
EleutherAI/gpt-neo-2.7B,EleutherAI,"['doi.org/10.5281/zenodo.5297715}', {'title': 'The Pile: An 800GB Dataset of Diverse Text for Language Modeling'}]"
facebook/opt-30b,facebook,['arxiv.org/abs/2205.01068']
microsoft/deberta-v2-xlarge,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
huggingface/CodeBERTa-small-v1,huggingface,"['arxiv.org/abs/1909.09436', {'title': '{CodeSearchNet'}]"
gpt2-large,huggingface,"['arxiv.org/abs/1910.09700', {'title': 'Language models are unsupervised multitask learners'}]"
sentence-transformers/paraphrase-multilingual-mpnet-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
facebook/blenderbot-400M-distill,facebook,['arxiv.org/abs/2004.13637']
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext,microsoft,['arxiv.org/abs/2007.15779']
facebook/m2m100_1.2B,facebook,['arxiv.org/abs/2010.11125']
sentence-transformers/paraphrase-MiniLM-L3-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
flair/ner-english-fast,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
albert-large-v2,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
google/mt5-base,google,['arxiv.org/abs/2010.11934']
google/bert_uncased_L-4_H-256_A-4,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
hfl/chinese-electra-180g-small-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
facebook/mbart-large-50-many-to-many-mmt,facebook,"['arxiv.org/abs/2008.00401', {'title': 'Multilingual Translation with Extensible Multilingual Pretraining and Finetuning'}]"
beomi/kcbert-base,beomi,"['arxiv.org/abs/1810.04805', {'title': 'KcBERT: Korean Comments BERT'}]"
pyannote/speaker-segmentation,pyannote,['bibtex']
cardiffnlp/twitter-xlm-roberta-base-sentiment,cardiffnlp,['arxiv.org/abs/2104.12250']
bigscience/T0_3B,bigscience,['arxiv.org/abs/2110.08207']
csebuetnlp/mT5_multilingual_XLSum,csebuetnlp,['bibtex']
sentence-transformers/paraphrase-xlm-r-multilingual-v1,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
google/fnet-base,google,"['arxiv.org/abs/2105.03824', 'bibtex']"
paulowoicho/t5-podcast-summarisation,paulowoicho,['arxiv.org/abs/2004.04270']
google/bert_uncased_L-2_H-256_A-4,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
google/bert_uncased_L-8_H-256_A-4,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
facebook/wav2vec2-base,facebook,['arxiv.org/abs/2006.11477']
sentence-transformers/paraphrase-MiniLM-L12-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/stsb-roberta-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
mrm8488/longformer-base-4096-finetuned-squadv2,mrm8488,['arxiv.org/abs/2004.05150']
microsoft/resnet-50,microsoft,"['arxiv.org/abs/1512.03385', {'title': 'Deep residual learning for image recognition'}]"
uer/t5-small-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer'}]"
facebook/bart-large-xsum,facebook,['arxiv.org/abs/1910.13461']
facebook/opt-350m,facebook,['arxiv.org/abs/2205.01068']
distilbert-base-uncased-distilled-squad,huggingface,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
dccuchile/bert-base-spanish-wwm-cased,dccuchile,"['arxiv.org/abs/1812.10464', {'title': 'Spanish Pre-Trained BERT Model and Evaluation Data'}]"
dbmdz/distilbert-base-turkish-cased,dbmdz,['arxiv.org/abs/1910.01108']
airesearch/wangchanberta-base-att-spm-uncased,airesearch,['arxiv.org/abs/1907.11692']
t5-3b,huggingface,"['arxiv.org/abs/1805.12471', 'bibtex']"
google/byt5-small,google,['arxiv.org/abs/1907.06292']
Langboat/mengzi-t5-base-mt,Langboat,['arxiv.org/abs/2110.06696']
bigscience/bloom-560m,bigscience,['arxiv.org/abs/1909.08053']
bert-large-uncased-whole-word-masking,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
albert-base-v1,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
microsoft/DialoGPT-medium,microsoft,['arxiv.org/abs/1911.00536']
t5-11b,huggingface,"['arxiv.org/abs/1805.12471', 'bibtex']"
microsoft/layoutlmv3-base,microsoft,"['arxiv.org/abs/2204.08387', {'title': 'LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking'}]"
nvidia/segformer-b0-finetuned-ade-512-512,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
sentence-transformers/distilbert-base-nli-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
Babelscape/rebel-large,Babelscape,['bibtex']
facebook/mbart-large-50-many-to-one-mmt,facebook,"['arxiv.org/abs/2008.00401', {'title': 'Multilingual Translation with Extensible Multilingual Pretraining and Finetuning'}]"
nvidia/stt_en_conformer_ctc_large,nvidia,['arxiv.org/abs/2005.08100']
sentence-transformers/nli-mpnet-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
flair/ner-english-ontonotes-fast,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
typeform/distilbert-base-uncased-mnli,typeform,['arxiv.org/abs/1910.09700']
DeepPavlov/rubert-base-cased,DeepPavlov,['arxiv.org/abs/1905.07213']
sentence-transformers/distiluse-base-multilingual-cased-v1,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
roberta-large-mnli,huggingface,"['arxiv.org/abs/1907.11692', {'title': 'RoBERTa: A Robustly Optimized BERT Pretraining Approach'}]"
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
microsoft/mdeberta-v3-base,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
hfl/chinese-macbert-base,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
oliverguhr/fullstop-punctuation-multilang-large,oliverguhr,[{'title': 'FullStop: Multilingual Deep Models for Punctuation Prediction'}]
vennify/t5-base-grammar-correction,vennify,['arxiv.org/abs/1702.04066']
prajjwal1/bert-mini,prajjwal1,"['arxiv.org/abs/1908.08962', 'bibtex']"
google/bigbird-roberta-large,google,['arxiv.org/abs/2007.14062']
openai/clip-vit-base-patch16,openai,['arxiv.org/abs/2103.00020']
sentence-transformers/distilbert-base-nli-stsb-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
tscholak/1zha5ono,tscholak,"['arxiv.org/abs/2109.05093', 'bibtex']"
flair/ner-german,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
microsoft/deberta-large,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
neuralmind/bert-large-portuguese-cased,neuralmind,['bibtex']
classla/bcms-bertic-ner,classla,['bibtex']
cmarkea/distilcamembert-base,cmarkea,"['arxiv.org/abs/1910.01108', 'bibtex']"
Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two,Hate-speech-CNERG,"['arxiv.org/abs/2012.10289', {'title': 'HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection'}]"
microsoft/cvt-13,microsoft,['arxiv.org/abs/2103.15808']
facebook/opt-1.3b,facebook,['arxiv.org/abs/2205.01068']
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract,microsoft,['arxiv.org/abs/2007.15779']
facebook/detr-resnet-50,facebook,"['arxiv.org/abs/2005.12872', 'bibtex']"
google/mt5-small,google,['arxiv.org/abs/2010.11934']
bigscience/T0pp,bigscience,['arxiv.org/abs/2110.08207']
nlpaueb/legal-bert-base-uncased,nlpaueb,['bibtex']
Narsil/deberta-large-mnli-zero-cls,Narsil,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
uer/roberta-base-finetuned-dianping-chinese,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
google/t5-v1_1-xl,google,['arxiv.org/abs/2002.05202']
sentence-transformers/all-distilroberta-v1,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
flair/pos-english,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
sentence-transformers/msmarco-bert-base-dot-v5,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/DialoGPT-small,microsoft,['arxiv.org/abs/1911.00536']
valhalla/t5-small-qg-hl,valhalla,['arxiv.org/abs/1910.10683']
microsoft/deberta-v3-small,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
google/mt5-large,google,['arxiv.org/abs/2010.11934']
albert-xxlarge-v2,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
DeepPavlov/rubert-base-cased-sentence,DeepPavlov,['arxiv.org/abs/1508.05326']
google/t5-large-lm-adapt,google,['arxiv.org/abs/2002.05202']
Rostlab/prot_t5_xl_uniref50,Rostlab,['doi.org/10.1101/2020.07.12.199554)']
sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
xlm-clm-ende-1024,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
klue/roberta-small,klue,['arxiv.org/abs/2105.09680']
nvidia/mit-b2,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
flair/ner-english,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/DialoGPT-large,microsoft,['arxiv.org/abs/1911.00536']
facebook/wmt19-en-de,facebook,"['arxiv.org/abs/1907.06616', 'bibtex']"
megagonlabs/transformers-ud-japanese-electra-base-ginza-510,megagonlabs,['bibtex']
openai-gpt,huggingface,"['arxiv.org/abs/1705.11168', {'title': 'Improving language understanding by generative pre-training'}]"
ufal/robeczech-base,ufal,['arxiv.org/abs/2105.11314']
facebook/hubert-large-ls960-ft,facebook,['arxiv.org/abs/2106.07447']
nlpaueb/bert-base-greek-uncased-v1,nlpaueb,"['arxiv.org/abs/2008.12014', 'doi.org/10.1145/3411408.3411440},', 'bibtex']"
sentence-transformers/distiluse-base-multilingual-cased,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
fnlp/bart-base-chinese,fnlp,[{'title': 'CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation'}]
GanjinZero/UMLSBert_ENG,GanjinZero,"['doi.org/10.1016/j.jbi.2021.103983},', {'title': 'CODER: Knowledge-infused cross-lingual medical term embedding for term normalization'}]"
facebook/wmt19-de-en,facebook,"['arxiv.org/abs/1907.06616', 'bibtex']"
facebook/wav2vec2-large-960h-lv60-self,facebook,['arxiv.org/abs/2010.11430']
cross-encoder/qnli-electra-base,cross-encoder,['arxiv.org/abs/1804.07461']
speechbrain/spkrec-ecapa-voxceleb,speechbrain,['bibtex']
sberbank-ai/mGPT,sberbank-ai,"['arxiv.org/abs/2204.07580', 'doi.org/10.48550/arxiv.2204.07580,']"
mrm8488/bert-small-finetuned-squadv2,mrm8488,['arxiv.org/abs/1908.08962']
microsoft/MiniLM-L12-H384-uncased,microsoft,['arxiv.org/abs/2002.10957']
pyannote/voice-activity-detection,pyannote,['bibtex']
joeddav/bart-large-mnli-yahoo-answers,joeddav,['arxiv.org/abs/1909.00161']
google/t5-v1_1-xxl,google,['arxiv.org/abs/2002.05202']
hfl/chinese-roberta-wwm-ext-large,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
mrm8488/bert-medium-finetuned-squadv2,mrm8488,['arxiv.org/abs/1908.08962']
prajjwal1/bert-small,prajjwal1,"['arxiv.org/abs/1908.08962', 'bibtex']"
hustvl/yolos-tiny,hustvl,"['arxiv.org/abs/2106.00666', 'bibtex']"
sentence-transformers/msmarco-distilbert-base-v4,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
facebook/wmt19-ru-en,facebook,"['arxiv.org/abs/1907.06616', 'bibtex']"
aubmindlab/bert-base-arabertv02,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
facebook/opt-6.7b,facebook,['arxiv.org/abs/2205.01068']
facebook/opt-2.7b,facebook,['arxiv.org/abs/2205.01068']
sentence-transformers/quora-distilbert-multilingual,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
facebook/detr-resnet-50-panoptic,facebook,"['arxiv.org/abs/2005.12872', 'bibtex']"
sentence-transformers/paraphrase-distilroberta-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/trocr-base-printed,microsoft,['arxiv.org/abs/2109.10282']
hfl/chinese-electra-180g-small-ex-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
dangvantuan/sentence-camembert-large,dangvantuan,"['arxiv.org/abs/1908.10084', {'title': 'Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks'}]"
google/bert_uncased_L-4_H-512_A-8,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
prajjwal1/bert-medium,prajjwal1,"['arxiv.org/abs/1908.08962', 'bibtex']"
allegro/herbert-base-cased,allegro,['bibtex']
google/t5-v1_1-large,google,['arxiv.org/abs/2002.05202']
bigscience/T0,bigscience,['arxiv.org/abs/2110.08207']
transfo-xl-wt103,huggingface,"['arxiv.org/abs/1901.02860', 'doi.org/10.48550/arxiv.1901.02860,']"
google/t5-v1_1-small,google,['arxiv.org/abs/2002.05202']
bert-large-cased-whole-word-masking-finetuned-squad,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
google/tapas-base-finetuned-wtq,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
naver-clova-ix/donut-base-finetuned-docvqa,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
etalab-ia/camembert-base-squadFR-fquad-piaf,etalab-ia,['bibtex']
ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli,ynie,['bibtex']
microsoft/swin-large-patch4-window12-384-in22k,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
microsoft/deberta-large-mnli,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
sentence-transformers/paraphrase-TinyBERT-L6-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
facebook/wav2vec2-large-xlsr-53,facebook,['arxiv.org/abs/2006.13979']
GroNLP/bert-base-dutch-cased,GroNLP,['arxiv.org/abs/1912.09582']
impira/layoutlm-document-qa,impira,['arxiv.org/abs/1912.13318']
unicamp-dl/ptt5-base-portuguese-vocab,unicamp-dl,[{'title': 'PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data'}]
flair/ner-multi,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
bigscience/bloom-1b7,bigscience,['arxiv.org/abs/1909.08053']
MilaNLProc/feel-it-italian-sentiment,MilaNLProc,"[{'title': '{""FEEL-IT: Emotion and Sentiment Classification for the Italian Language""'}]"
microsoft/wavlm-large,microsoft,['arxiv.org/abs/1912.07875']
sbcBI/sentiment_analysis_model,sbcBI,['arxiv.org/abs/1810.04805']
sentence-transformers/paraphrase-albert-small-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/deberta-xlarge-mnli,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
Salesforce/codegen-2B-multi,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
Salesforce/codegen-350M-mono,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
microsoft/dit-base-finetuned-rvlcdip,microsoft,"['arxiv.org/abs/2203.02378', {'title': 'Building a test collection for complex document information processing'}]"
google/long-t5-tglobal-base,google,[{'title': 'LongT5: Efficient Text-To-Text Transformer for Long Sequences'}]
Salesforce/codet5-large,Salesforce,"['arxiv.org/abs/1909.09436', 'bibtex']"
studio-ousia/luke-base,studio-ousia,"['arxiv.org/abs/1906.08237', {'title': 'LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention'}]"
valhalla/t5-base-qa-qg-hl,valhalla,['arxiv.org/abs/1910.10683']
hfl/chinese-bert-wwm,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
cardiffnlp/twitter-xlm-roberta-base,cardiffnlp,['arxiv.org/abs/2104.12250']
sentence-transformers/bert-base-nli-stsb-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
valhalla/t5-small-e2e-qg,valhalla,['arxiv.org/abs/1910.10683']
aubmindlab/bert-base-arabertv2,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
MilaNLProc/feel-it-italian-emotion,MilaNLProc,"[{'title': '{""FEEL-IT: Emotion and Sentiment Classification for the Italian Language""'}]"
papluca/xlm-roberta-base-language-detection,papluca,['arxiv.org/abs/1911.02116']
google/t5-xl-lm-adapt,google,['arxiv.org/abs/2002.05202']
hfl/chinese-electra-180g-base-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
microsoft/deberta-v3-xsmall,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
pierreguillou/bert-large-cased-squad-v1.1-portuguese,pierreguillou,"[{'title': 'Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1'}]"
Salesforce/codet5-small,Salesforce,['arxiv.org/abs/2109.00859']
facebook/hubert-base-ls960,facebook,['arxiv.org/abs/2106.07447']
sentence-transformers/paraphrase-distilroberta-base-v1,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/stsb-mpnet-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
valhalla/longformer-base-4096-finetuned-squadv1,valhalla,['arxiv.org/abs/2004.05150']
flair/ner-french,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
microsoft/deberta-xlarge,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
facebook/opt-66b,facebook,['arxiv.org/abs/2205.01068']
microsoft/swin-base-patch4-window7-224-in22k,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
cmarkea/distilcamembert-base-ner,cmarkea,['bibtex']
sentence-transformers/all-roberta-large-v1,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
sentence-transformers/stsb-roberta-base,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
flair/chunk-english,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
KoboldAI/GPT-Neo-2.7B-Shinen,KoboldAI,['doi.org/10.5281/zenodo.5297715}']
dandelin/vilt-b32-finetuned-vqa,dandelin,['arxiv.org/abs/2102.03334']
facebook/wav2vec2-large-960h,facebook,['arxiv.org/abs/2006.11477']
facebook/blenderbot_small-90M,facebook,['arxiv.org/abs/1907.06616']
IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
klue/roberta-large,klue,['arxiv.org/abs/2105.09680']
Rostlab/prot_bert_bfd,Rostlab,['doi.org/10.1101/2020.07.12.199554)']
sentence-transformers/msmarco-roberta-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
indobenchmark/indobert-base-p2,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
sentence-transformers/stsb-distilbert-base,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
facebook/wav2vec2-xlsr-53-espeak-cv-ft,facebook,['arxiv.org/abs/2109.11680']
SkolkovoInstitute/roberta_toxicity_classifier,SkolkovoInstitute,['arxiv.org/abs/1907.11692']
yangheng/deberta-v3-base-absa-v1.1,yangheng,"['arxiv.org/abs/2110.08604', 'bibtex']"
DeepPavlov/distilrubert-base-cased-conversational,DeepPavlov,"['arxiv.org/abs/2205.02340', 'doi.org/10.48550/arxiv.2205.02340,']"
nvidia/mit-b0,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
pdelobelle/robbert-v2-dutch-base,pdelobelle,"['arxiv.org/abs/2001.06286', 'bibtex']"
sentence-transformers/stsb-roberta-large,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
HooshvareLab/bert-fa-base-uncased,HooshvareLab,"['arxiv.org/abs/2005.12515', {'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]"
valhalla/t5-small-qa-qg-hl,valhalla,['arxiv.org/abs/1910.10683']
Maltehb/aelaectra-danish-electra-small-cased-ner-dane,Maltehb,['arxiv.org/abs/2003.10555']
Salesforce/codet5-base,Salesforce,['arxiv.org/abs/2109.00859']
redewiedergabe/bert-base-historical-german-rw-cased,redewiedergabe,['arxiv.org/abs/1508.01991']
facebook/mbart-large-50-one-to-many-mmt,facebook,"['arxiv.org/abs/2008.00401', {'title': 'Multilingual Translation with Extensible Multilingual Pretraining and Finetuning'}]"
indobenchmark/indobert-base-p1,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
hfl/rbt6,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
sentence-transformers/msmarco-distilbert-cos-v5,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
nlpaueb/bert-base-uncased-contracts,nlpaueb,['bibtex']
google/t5-base-lm-adapt,google,['arxiv.org/abs/2002.05202']
flaubert/flaubert_base_cased,flaubert,[{'title': 'FlauBERT: des mod{\\`e'}]
facebook/blenderbot-3B,facebook,['arxiv.org/abs/1907.06616']
ai4bharat/IndicBART,ai4bharat,['arxiv.org/abs/2109.02903']
xlm-roberta-large-finetuned-conll03-german,huggingface,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
sentence-transformers/stsb-xlm-r-multilingual,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
bigscience/bloom-3b,bigscience,['arxiv.org/abs/1909.08053']
hfl/rbt3,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
klue/roberta-base,klue,['arxiv.org/abs/2105.09680']
microsoft/beit-base-patch16-224-pt22k-ft22k,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
bigscience/bloom,bigscience,['arxiv.org/abs/1909.08053']
facebook/vit-mae-base,facebook,"['arxiv.org/abs/2111.06377', 'bibtex']"
KES/T5-KES,KES,['arxiv.org/abs/1702.04066']
sbcBI/sentiment_analysis,sbcBI,['arxiv.org/abs/1810.04805']
xlnet-large-cased,huggingface,"['arxiv.org/abs/1906.08237', 'bibtex']"
sentence-transformers/distilroberta-base-paraphrase-v1,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
camembert/camembert-base,camembert,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
ctrl,huggingface,"['arxiv.org/abs/1909.05858', {'title': '{CTRL - A Conditional Transformer Language Model for Controllable Generation'}]"
mrm8488/bert-tiny-finetuned-squadv2,mrm8488,['arxiv.org/abs/1908.08962']
nghuyong/ernie-2.0-base-en,nghuyong,"['arxiv.org/abs/1907.12412', {'title': 'ERNIE 2.0: A Continual Pre-training Framework for Language Understanding'}]"
pucpr/clinicalnerpt-disorder,pucpr,['bibtex']
UBC-NLP/MARBERT,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
Langboat/mengzi-t5-base,Langboat,['arxiv.org/abs/2110.06696']
facebook/hubert-large-ll60k,facebook,['arxiv.org/abs/2106.07447']
SkolkovoInstitute/bart-base-detox,SkolkovoInstitute,['bibtex']
laion/CLIP-ViT-H-14-laion2B-s32B-b79K,laion,"['arxiv.org/abs/1910.04867', 'doi.org/10.5281/zenodo.5143773}', {'title': 'Learning Transferable Visual Models From Natural Language Supervision'}]"
google/vit-base-patch16-384,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
RUCAIBox/mvp,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
TalTechNLP/voxlingua107-epaca-tdnn,TalTechNLP,[{'title': '{VoxLingua107'}]
google/canine-s,google,"['arxiv.org/abs/2103.06874', 'bibtex']"
pritamdeka/S-Biomed-Roberta-snli-multinli-stsb,pritamdeka,[{'title': 'Sentence-bert: Sentence embeddings using siamese bert-networks'}]
flaubert/flaubert_base_uncased,flaubert,[{'title': 'FlauBERT: des mod{\\`e'}]
facebook/blenderbot-1B-distill,facebook,['arxiv.org/abs/1907.06616']
google/bigbird-pegasus-large-arxiv,google,['arxiv.org/abs/2007.14062']
hfl/chinese-macbert-large,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
sentence-transformers/roberta-large-nli-stsb-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
google/t5-small-lm-adapt,google,['arxiv.org/abs/2002.05202']
allenai/biomed_roberta_base,allenai,['bibtex']
facebook/opt-13b,facebook,['arxiv.org/abs/2205.01068']
Geotrend/bert-base-fr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
microsoft/swin-tiny-patch4-window7-224,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
ntu-spml/distilhubert,ntu-spml,['arxiv.org/abs/2110.01900']
izumi-lab/electra-small-japanese-fin-generator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
DeepPavlov/distilrubert-tiny-cased-conversational,DeepPavlov,"['arxiv.org/abs/2205.02340', 'doi.org/10.48550/arxiv.2205.02340,']"
digitalepidemiologylab/covid-twitter-bert-v2,digitalepidemiologylab,[{'title': 'COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter'}]
facebook/s2t-small-librispeech-asr,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
facebook/wav2vec2-large-lv60,facebook,['arxiv.org/abs/2006.11477']
uer/sbert-base-chinese-nli,uer,"['arxiv.org/abs/1909.05658', {'title': 'Sentence-bert: Sentence embeddings using siamese bert-networks'}]"
facebook/s2t-medium-librispeech-asr,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
ai4bharat/indic-bert,ai4bharat,"[{'title': '{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages'}]"
kakaobrain/kogpt,kakaobrain,['arxiv.org/abs/2104.09864']
microsoft/Multilingual-MiniLM-L12-H384,microsoft,['arxiv.org/abs/2002.10957']
microsoft/BiomedVLP-CXR-BERT-general,microsoft,"['arxiv.org/abs/2204.09817', 'doi.org/10.48550/arxiv.2204.09817,']"
nlpaueb/bert-base-uncased-eurlex,nlpaueb,['bibtex']
facebook/wav2vec2-xls-r-300m,facebook,['arxiv.org/abs/2111.09296']
google/owlvit-base-patch32,google,"['arxiv.org/abs/2205.06230', {'title': 'Simple Open-Vocabulary Object Detection with Vision Transformers'}]"
ml6team/keyphrase-extraction-kbir-inspec,ml6team,['arxiv.org/abs/2112.08547']
bigscience/bloom-7b1,bigscience,['arxiv.org/abs/1909.08053']
onlplab/alephbert-base,onlplab,['arxiv.org/abs/1810.04805']
microsoft/trocr-large-printed,microsoft,['arxiv.org/abs/2109.10282']
cointegrated/LaBSE-en-ru,cointegrated,['arxiv.org/abs/2007.01852']
Salesforce/codegen-350M-multi,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
pysentimiento/robertuito-emotion-analysis,pysentimiento,[{'title': 'EmoEvent: A multilingual emotion corpus based on different events'}]
Rostlab/prot_t5_xl_half_uniref50-enc,Rostlab,['doi.org/10.1101/2020.07.12.199554)']
indolem/indobert-base-uncased,indolem,[{'title': 'IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP'}]
microsoft/swin-large-patch4-window7-224,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
nlpaueb/legal-bert-small-uncased,nlpaueb,['bibtex']
microsoft/deberta-base-mnli,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
facebook/incoder-6B,facebook,['arxiv.org/abs/2204.05999']
google/t5-large-ssm-nq,google,['arxiv.org/abs/1910.10683']
flax-sentence-embeddings/all_datasets_v4_MiniLM-L6,flax-sentence-embeddings,"['arxiv.org/abs/1810.09305', 'doi.org/10.18653/v1/d15-1075),']"
allenai/tk-instruct-3b-def-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
google/t5-xxl-lm-adapt,google,['arxiv.org/abs/2002.05202']
DeepPavlov/distilrubert-tiny-cased-conversational-v1,DeepPavlov,"['arxiv.org/abs/2205.02340', 'doi.org/10.48550/arxiv.2205.02340,']"
voidism/diffcse-roberta-base-sts,voidism,"['arxiv.org/abs/2204.10298', 'doi.org/10.48550/arXiv.2204.10298)', {'title': '{DiffCSE'}]"
pyronear/rexnet1_3x,pyronear,"['arxiv.org/abs/2007.00992', 'bibtex']"
espnet/kan-bayashi_ljspeech_vits,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
albert-xxlarge-v1,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
human-centered-summarization/financial-summarization-pegasus,human-centered-summarization,['bibtex']
j-hartmann/sentiment-roberta-large-english-3-classes,j-hartmann,[{'title': 'The Power of Brand Selfies'}]
facebook/muppet-roberta-large,facebook,"['arxiv.org/abs/2101.11038', 'bibtex']"
flair/ner-dutch,flair,['bibtex']
sentence-transformers/clip-ViT-B-32-multilingual-v1,sentence-transformers,"['arxiv.org/abs/2004.09813', 'bibtex']"
uer/chinese_roberta_L-4_H-512,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
bvanaken/clinical-assertion-negation-bert,bvanaken,['bibtex']
facebook/deit-small-distilled-patch16-224,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
Rostlab/prot_t5_xl_bfd,Rostlab,['doi.org/10.1101/2020.07.12.199554)']
naver-clova-ix/donut-base,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
dandelin/vilt-b32-mlm,dandelin,['arxiv.org/abs/2102.03334']
roberta-base-openai-detector,huggingface,"['arxiv.org/abs/1904.09751', {'title': 'Release strategies and the social impacts of language models'}]"
facebook/convnext-tiny-224,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
uer/roberta-base-chinese-extractive-qa,uer,[{'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]
Helsinki-NLP/opus-mt-tc-big-en-pt,Helsinki-NLP,['bibtex']
shibing624/macbert4csc-base-chinese,shibing624,['arxiv.org/abs/2004.13922']
Salesforce/codegen-16B-mono,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
cointegrated/rubert-tiny-toxicity,cointegrated,['arxiv.org/abs/2103.05345']
uer/roberta-base-finetuned-cluener2020-chinese,uer,[{'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]
microsoft/beit-base-patch16-224-pt22k,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
laion/CLIP-ViT-B-32-laion2B-s34B-b79K,laion,"['arxiv.org/abs/1910.04867', 'doi.org/10.5281/zenodo.5143773}', {'title': 'Learning Transferable Visual Models From Natural Language Supervision'}]"
google/long-t5-local-base,google,[{'title': 'LongT5: Efficient Text-To-Text Transformer for Long Sequences'}]
microsoft/unispeech-sat-base,microsoft,['arxiv.org/abs/2110.05752']
facebook/detr-resnet-101,facebook,"['arxiv.org/abs/2005.12872', 'bibtex']"
google/bigbird-pegasus-large-bigpatent,google,['arxiv.org/abs/2007.14062']
google/muril-base-cased,google,['arxiv.org/abs/2103.10730']
xlm-mlm-en-2048,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
microsoft/dit-base,microsoft,"['arxiv.org/abs/2203.02378', {'title': 'Building a test collection for complex document information processing'}]"
microsoft/xtremedistil-l6-h256-uncased,microsoft,['arxiv.org/abs/2106.04563']
apanc/russian-inappropriate-messages,apanc,['bibtex']
Seznam/small-e-czech,Seznam,['arxiv.org/abs/2003.10555']
Salesforce/codegen-2B-mono,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
facebook/vit-mae-large,facebook,"['arxiv.org/abs/2111.06377', 'bibtex']"
pyannote/overlapped-speech-detection,pyannote,['bibtex']
izumi-lab/bert-small-japanese-fin,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
flaubert/flaubert_small_cased,flaubert,[{'title': 'FlauBERT: des mod{\\`e'}]
microsoft/trocr-base-handwritten,microsoft,['arxiv.org/abs/2109.10282']
optimum/sbert-all-MiniLM-L6-with-pooler,optimum,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
facebook/incoder-1B,facebook,['arxiv.org/abs/2204.05999']
facebook/blenderbot-90M,facebook,['arxiv.org/abs/1907.06616']
lgris/wav2vec2-large-xlsr-open-brazilian-portuguese-v2,lgris,['arxiv.org/abs/2012.03411']
facebook/fastspeech2-en-ljspeech,facebook,"['arxiv.org/abs/2006.04558', 'bibtex']"
megagonlabs/t5-base-japanese-web,megagonlabs,['bibtex']
google/ul2,google,['arxiv.org/abs/2205.05131']
microsoft/deberta-v2-xlarge-mnli,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
camembert/camembert-large,camembert,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
asafaya/hubert-large-arabic,asafaya,['arxiv.org/abs/2106.07447']
Geotrend/distilbert-base-es-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
microsoft/prophetnet-large-uncased,microsoft,"['arxiv.org/abs/2001.04063', {'title': 'Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training'}]"
qanastek/pos-french,qanastek,['bibtex']
google/tapas-large-finetuned-wtq,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
Salesforce/codegen-6B-mono,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
facebook/flava-full,facebook,['arxiv.org/abs/2112.04482']
MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli,MoritzLaurer,['arxiv.org/abs/2111.09543']
microsoft/BiomedVLP-CXR-BERT-specialized,microsoft,"['arxiv.org/abs/2204.09817', 'doi.org/10.48550/arxiv.2204.09817,']"
T-Systems-onsite/cross-en-de-roberta-sentence-transformer,T-Systems-onsite,['arxiv.org/abs/1908.10084']
yikuan8/Clinical-Longformer,yikuan8,[{'title': 'Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences'}]
microsoft/deberta-v2-xxlarge-mnli,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
zlucia/custom-legalbert,zlucia,"['arxiv.org/abs/2104.08671', {'title': 'When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset'}]"
asafaya/bert-base-arabic,asafaya,['bibtex']
huggingface/CodeBERTa-language-id,huggingface,"['arxiv.org/abs/1909.09436', {'title': '{CodeSearchNet'}]"
moussaKam/mbarthez,moussaKam,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
bert-large-cased-whole-word-masking,huggingface,"['arxiv.org/abs/1810.04805', 'bibtex']"
facebook/data2vec-audio-base-960h,facebook,['arxiv.org/abs/2202.03555']
microsoft/deberta-v2-xxlarge,microsoft,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
inokufu/flaubert-base-uncased-xnli-sts,inokufu,['arxiv.org/abs/1809.05053']
uer/gpt2-chinese-lyric,uer,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
fnlp/bart-large-chinese,fnlp,[{'title': 'CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation'}]
setu4993/LaBSE,setu4993,['arxiv.org/abs/2007.01852']
Salesforce/codegen-6B-multi,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
TurkuNLP/bert-base-finnish-uncased-v1,TurkuNLP,['arxiv.org/abs/1912.07076']
oigele/Fb_improved_zeroshot,oigele,['arxiv.org/abs/1909.00161']
naver/splade-cocondenser-ensembledistil,naver,"['arxiv.org/abs/2205.04733', 'doi.org/10.48550/arxiv.2205.04733,']"
nghuyong/ernie-1.0-base-zh,nghuyong,"['arxiv.org/abs/1904.09223', {'title': 'Ernie: Enhanced representation through knowledge integration'}]"
facebook/mgenre-wiki,facebook,"['arxiv.org/abs/2103.12528', 'doi.org/10.1162/tacl\\_a\\_00460},', 'bibtex']"
finiteautomata/bertweet-base-emotion-analysis,finiteautomata,"['arxiv.org/abs/2106.09462', {'title': 'EmoEvent: A multilingual emotion corpus based on different events'}]"
qarib/bert-base-qarib,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
patrickvonplaten/data2vec-audio-base-10m-4-gram,patrickvonplaten,['arxiv.org/abs/2202.03555']
facebook/dino-vitb16,facebook,"['arxiv.org/abs/2010.11929', 'bibtex']"
uer/chinese_roberta_L-4_H-256,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
pszemraj/long-t5-tglobal-base-16384-book-summary,pszemraj,['arxiv.org/abs/2105.08209']
sentence-transformers/roberta-base-nli-stsb-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/msmarco-distilbert-dot-v5,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
mio/amadeus,mio,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
microsoft/beit-base-patch16-224,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
PlanTL-GOB-ES/roberta-base-bne,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
valhalla/t5-base-qg-hl,valhalla,['arxiv.org/abs/1910.10683']
zlucia/legalbert,zlucia,"['arxiv.org/abs/2104.08671', {'title': 'When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset'}]"
bhadresh-savani/bert-base-uncased-emotion,bhadresh-savani,['arxiv.org/abs/1810.04805']
sentence-transformers/stsb-distilroberta-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
KoboldAI/GPT-NeoX-20B-Erebus,KoboldAI,"['arxiv.org/abs/2204.06745', {'title': '{GPT-NeoX-20B'}]"
facebook/wav2vec2-large-960h-lv60,facebook,['arxiv.org/abs/2006.11477']
dbmdz/bert-mini-historic-multilingual-cased,dbmdz,['arxiv.org/abs/1908.08962']
jjzha/jobspanbert-base-cased,jjzha,['bibtex']
roberta-large-openai-detector,huggingface,"['arxiv.org/abs/1904.09751', {'title': 'Release strategies and the social impacts of language models'}]"
Langboat/mengzi-bert-base,Langboat,['arxiv.org/abs/2110.06696']
pucpr/biobertpt-clin,pucpr,['bibtex']
uer/roberta-base-finetuned-chinanews-chinese,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
microsoft/layoutlmv3-large,microsoft,"['arxiv.org/abs/2204.08387', {'title': 'LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking'}]"
akhooli/gpt2-small-arabic-poetry,akhooli,['bibtex']
nvidia/mit-b3,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
MCG-NJU/videomae-base,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
lincoln/mbart-mlsum-automatic-summarization,lincoln,[{'title': 'MLSUM: The Multilingual Summarization Corpus'}]
espnet/kamo-naoyuki-mini_an4_asr_train_raw_bpe_valid.acc.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
hfl/chinese-xlnet-base,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
microsoft/xtremedistil-l6-h384-uncased,microsoft,['arxiv.org/abs/2106.04563']
cmarkea/distilcamembert-base-qa,cmarkea,['bibtex']
facebook/deit-base-distilled-patch16-224,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
uer/chinese_roberta_L-2_H-128,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
jjzha/jobbert-base-cased,jjzha,['bibtex']
sentence-transformers/all-mpnet-base-v1,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary,MoritzLaurer,['arxiv.org/abs/2104.07179']
dbmdz/convbert-base-turkish-mc4-cased,dbmdz,['doi.org/10.5281/zenodo.3770924}']
gerulata/slovakbert,gerulata,['arxiv.org/abs/2109.15254']
aubmindlab/bert-base-arabertv01,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
nvidia/segformer-b5-finetuned-cityscapes-1024-1024,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
google/byt5-base,google,['arxiv.org/abs/1907.06292']
nghuyong/ernie-3.0-base-zh,nghuyong,"['arxiv.org/abs/2107.02137', {'title': 'Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation'}]"
HooshvareLab/bert-fa-zwnj-base,HooshvareLab,"['arxiv.org/abs/2005.12515', {'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]"
google/bert_uncased_L-6_H-128_A-2,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
mrm8488/codebert-base-finetuned-detect-insecure-code,mrm8488,['arxiv.org/abs/2002.08155']
nvidia/segformer-b5-finetuned-ade-640-640,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
aubmindlab/bert-large-arabertv02,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
uer/chinese_roberta_L-12_H-768,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
bigscience/bloom-1b1,bigscience,['arxiv.org/abs/1909.08053']
Salesforce/codet5-base-multi-sum,Salesforce,"['arxiv.org/abs/2109.00859', {'title': 'CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation'}]"
hf-internal-testing/tiny-vilt-random-vqa,hf-internal-testing,['arxiv.org/abs/2102.03334']
microsoft/wavlm-base-plus,microsoft,['arxiv.org/abs/1912.07875']
flair/ner-english-ontonotes,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
sentence-transformers/sentence-t5-base,sentence-transformers,['arxiv.org/abs/2108.08877']
sentence-transformers/msmarco-distilbert-base-v3,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
jhu-clsp/bibert-ende,jhu-clsp,['bibtex']
microsoft/beit-base-patch16-384,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
michiyasunaga/BioLinkBERT-base,michiyasunaga,['arxiv.org/abs/2203.15827']
KoboldAI/fairseq-dense-125M,KoboldAI,['arxiv.org/abs/2112.10684']
sebastian-hofstaetter/colbert-distilbert-margin_mse-T2-msmarco,sebastian-hofstaetter,['arxiv.org/abs/2010.02666']
laion/CLIP-ViT-L-14-laion2B-s32B-b82K,laion,"['arxiv.org/abs/2110.09456', 'doi.org/10.5281/zenodo.5143773}', {'title': 'Learning Transferable Visual Models From Natural Language Supervision'}]"
indolem/indobertweet-base-uncased,indolem,[{'title': 'IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization'}]
l3cube-pune/hing-mbert,l3cube-pune,['arxiv.org/abs/2204.08398']
flair/upos-english,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
rinna/japanese-clip-vit-b-16,rinna,['arxiv.org/abs/2103.00020']
Salesforce/codegen-16B-multi,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
facebook/wmt19-en-ru,facebook,"['arxiv.org/abs/1907.06616', 'bibtex']"
google/owlvit-large-patch14,google,"['arxiv.org/abs/2205.06230', {'title': 'Simple Open-Vocabulary Object Detection with Vision Transformers'}]"
valhalla/bart-large-finetuned-squadv1,valhalla,['arxiv.org/abs/1910.13461']
uer/roberta-base-word-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
google/vit-base-patch32-224-in21k,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
saattrupdan/nbailab-base-ner-scandi,saattrupdan,['arxiv.org/abs/1911.12146']
studio-ousia/luke-large,studio-ousia,"['arxiv.org/abs/1906.08237', {'title': 'LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention'}]"
aubmindlab/bert-base-arabertv02-twitter,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
xlm-mlm-100-1280,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
uer/gpt2-chinese-poem,uer,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
microsoft/trocr-small-printed,microsoft,['arxiv.org/abs/2109.10282']
google/vit-large-patch32-384,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
uer/bart-base-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension'}]"
google/bert_uncased_L-8_H-512_A-8,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
classla/bcms-bertic,classla,['bibtex']
microsoft/wavlm-base-plus-sv,microsoft,['arxiv.org/abs/1912.07875']
sentence-transformers/msmarco-distilbert-base-dot-prod-v3,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
hfl/chinese-electra-180g-large-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
naver-clova-ix/donut-base-finetuned-cord-v2,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
cointegrated/rubert-tiny2-cedr-emotion-detection,cointegrated,['doi.org/10.1016/j.procs.2021.06.075)']
facebook/data2vec-text-base,facebook,"['arxiv.org/abs/2202.03555', 'doi.org/10.48550/arxiv.2202.03555,']"
kamalkraj/deberta-base,kamalkraj,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
sentence-transformers/all-MiniLM-L12-v1,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
microsoft/beit-large-patch16-512,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
CompVis/ldm-text2im-large-256,CompVis,['arxiv.org/abs/2112.10752']
KoboldAI/GPT-NeoX-20B-Skein,KoboldAI,"['arxiv.org/abs/2204.06745', {'title': '{GPT-NeoX-20B'}]"
KoboldAI/GPT-J-6B-Shinen,KoboldAI,['arxiv.org/abs/2101.00027']
sentence-transformers/nq-distilbert-base-v1,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
izumi-lab/bert-small-japanese,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
Norod78/hebrew-bad_wiki-gpt_neo-tiny,Norod78,['arxiv.org/abs/1910.09700']
KoboldAI/fairseq-dense-2.7B,KoboldAI,['arxiv.org/abs/2112.10684']
Babelscape/wikineural-multilingual-ner,Babelscape,['bibtex']
snunlp/KR-BERT-char16424,snunlp,"['arxiv.org/abs/2008.03979', {'title': 'KR-BERT: A Small-Scale Korean-Specific Language Model'}]"
pysentimiento/robertuito-hate-speech,pysentimiento,[{'title': 'SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter'}]
audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim,audeering,['arxiv.org/abs/2203.07378']
microsoft/layoutlmv2-large-uncased,microsoft,['arxiv.org/abs/2012.14740']
KoboldAI/GPT-Neo-1.3B-Adventure,KoboldAI,['doi.org/10.5281/zenodo.5297715}']
google/t5-small-ssm-nq,google,['arxiv.org/abs/1910.10683']
facebook/data2vec-audio-large,facebook,['arxiv.org/abs/2202.03555']
flax-community/t5-large-wikisplit,flax-community,['arxiv.org/abs/1907.12461']
cmarkea/distilcamembert-base-sentiment,cmarkea,['bibtex']
eugenesiow/bart-paraphrase,eugenesiow,['arxiv.org/abs/1910.13461']
GroNLP/hateBERT,GroNLP,['bibtex']
moussaKam/barthez,moussaKam,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
csebuetnlp/banglabert,csebuetnlp,"['arxiv.org/abs/2101.00204', {'title': 'BanglaBERT: Lagnuage Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla'}]"
eugenesiow/edsr-base,eugenesiow,['arxiv.org/abs/1707.02921']
facebook/deit-small-patch16-224,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
AnReu/math_albert,AnReu,[{'title': 'Transformer-Encoder and Decoder Models for Questions on Math'}]
aubmindlab/bert-large-arabertv02-twitter,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
google/bigbird-pegasus-large-pubmed,google,['arxiv.org/abs/2007.14062']
MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7,MoritzLaurer,[{'title': 'Less {Annotating'}]
facebook/hubert-xlarge-ls960-ft,facebook,['arxiv.org/abs/2106.07447']
Narrativa/byt5-base-tweet-hate-detection,Narrativa,['arxiv.org/abs/1907.06292']
nvidia/mit-b1,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
microsoft/beit-base-finetuned-ade-640-640,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
google/vit-large-patch32-224-in21k,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
etalab-ia/dpr-ctx_encoder-fr_qa-camembert,etalab-ia,"['arxiv.org/abs/2004.04906', 'bibtex']"
etalab-ia/dpr-question_encoder-fr_qa-camembert,etalab-ia,"['arxiv.org/abs/2004.04906', 'bibtex']"
EMBEDDIA/crosloengual-bert,EMBEDDIA,"['arxiv.org/abs/2006.07890', 'doi.org/10.1007/978-3-030-58323-1_11"",']"
model-attribution-challenge/gpt2,model-attribution-challenge,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
Langboat/mengzi-bert-base-fin,Langboat,['arxiv.org/abs/2110.06696']
google/ddpm-celebahq-256,google,['arxiv.org/abs/2006.11239']
EleutherAI/enformer-official-rough,EleutherAI,['doi.org/10.1038/s41592-021-01252-x']
speechbrain/spkrec-xvect-voxceleb,speechbrain,['bibtex']
asi/gpt-fr-cased-base,asi,['bibtex']
facebook/wav2vec2-large-robust-ft-swbd-300h,facebook,['arxiv.org/abs/2104.01027']
sentence-transformers/bert-base-wikipedia-sections-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
PlanTL-GOB-ES/roberta-large-bne,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
google/mt5-xl,google,['arxiv.org/abs/2010.11934']
pierreguillou/bert-base-cased-squad-v1.1-portuguese,pierreguillou,"[{'title': 'Portuguese BERT base cased QA (Question Answering), finetuned on SQUAD v1.1'}]"
voidful/dpr-question_encoder-bert-base-multilingual,voidful,['arxiv.org/abs/2004.04906']
LeBenchmark/wav2vec2-FR-7K-large,LeBenchmark,[{'title': 'LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech'}]
kamalkraj/deberta-v2-xlarge,kamalkraj,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
nvidia/mit-b5,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
facebook/maskformer-swin-base-coco,facebook,['arxiv.org/abs/2107.06278']
uer/t5-v1_1-base-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer'}]"
google/long-t5-tglobal-large,google,[{'title': 'LongT5: Efficient Text-To-Text Transformer for Long Sequences'}]
sentence-transformers/msmarco-MiniLM-L-6-v3,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
facebook/deit-base-patch16-224,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
facebook/muppet-roberta-base,facebook,"['arxiv.org/abs/2101.11038', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
allegro/herbert-large-cased,allegro,['bibtex']
tner/twitter-roberta-base-dec2021-tweetner7-random,tner,['bibtex']
voidful/dpr-ctx_encoder-bert-base-multilingual,voidful,['arxiv.org/abs/2004.04906']
PlanTL-GOB-ES/roberta-base-biomedical-clinical-es,PlanTL-GOB-ES,['arxiv.org/abs/2109.03570']
nvidia/stt_en_conformer_transducer_xlarge,nvidia,['arxiv.org/abs/2005.08100']
PlanTL-GOB-ES/roberta-base-bne-sqac,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
apple/mobilevit-small,apple,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
uer/chinese_roberta_L-8_H-512,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
UBC-NLP/ARBERT,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
ThomasNLG/t5-qa_squad2neg-en,ThomasNLG,"['arxiv.org/abs/2103.12693', {'title': 'QuestEval: Summarization Asks for Fact-based Evaluation'}]"
facebook/s2t-small-mustc-en-fr-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
facebook/xglm-564M,facebook,['arxiv.org/abs/2112.10668']
sentence-transformers/nli-roberta-base,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
AI-Growth-Lab/PatentSBERTa,AI-Growth-Lab,"['arxiv.org/abs/2103.11933', {'title': 'PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT'}]"
amberoad/bert-multilingual-passage-reranking-msmarco,amberoad,['arxiv.org/abs/1901.04085']
sentence-transformers/msmarco-distilbert-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
tscholak/cxmefzzi,tscholak,"['arxiv.org/abs/2109.05093', 'bibtex']"
facebook/wav2vec2-large-robust-ft-libri-960h,facebook,['arxiv.org/abs/2104.01027']
phiyodr/bert-base-finetuned-squad2,phiyodr,['arxiv.org/abs/1810.04805']
albert-xlarge-v2,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut,wannaphong,['arxiv.org/abs/2208.04799']
alexyalunin/RuBioRoBERTa,alexyalunin,['arxiv.org/abs/2204.03951']
pysentimiento/bertweet-hate-speech,pysentimiento,['arxiv.org/abs/2106.09462']
eugenesiow/msrn-bam,eugenesiow,['arxiv.org/abs/2104.07566']
readerbench/RoBERT-base,readerbench,[{'title': 'RoBERT--A Romanian BERT Model'}]
google/mt5-xxl,google,['arxiv.org/abs/2010.11934']
microsoft/xtremedistil-l12-h384-uncased,microsoft,['arxiv.org/abs/2106.04563']
aubmindlab/araelectra-base-discriminator,aubmindlab,"['arxiv.org/abs/2012.15516', 'bibtex']"
voidism/diffcse-bert-base-uncased-sts,voidism,"['arxiv.org/abs/2204.10298', 'doi.org/10.48550/arXiv.2204.10298)', {'title': '{DiffCSE'}]"
google/t5-efficient-tiny,google,['arxiv.org/abs/2109.10686']
alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli,alan-turing-institute,['arxiv.org/abs/2010.11934']
facebook/roberta-hate-speech-dynabench-r4-target,facebook,"['arxiv.org/abs/2012.15761', {'title': 'Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection'}]"
speechbrain/tts-hifigan-ljspeech,speechbrain,['arxiv.org/abs/2010.05646']
ltgoslo/norbert,ltgoslo,['arxiv.org/abs/2104.06546']
LorenzoDeMattei/GePpeTto,LorenzoDeMattei,['arxiv.org/abs/2004.14253']
IlyaGusev/mbart_ru_sum_gazeta,IlyaGusev,['arxiv.org/abs/2006.11063']
deepmind/language-perceiver,deepmind,"['arxiv.org/abs/1810.04805', 'bibtex']"
facebook/s2t-large-librispeech-asr,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
model-attribution-challenge/gpt-neo-125M,model-attribution-challenge,"['doi.org/10.5281/zenodo.5297715}', {'title': 'The Pile: An 800GB Dataset of Diverse Text for Language Modeling'}]"
hfl/rbtl3,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
sentence-transformers/bert-base-nli-cls-token,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/distilroberta-base-msmarco-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
hustvl/yolos-small,hustvl,"['arxiv.org/abs/2106.00666', 'bibtex']"
sentence-transformers/all-MiniLM-L6-v1,sentence-transformers,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
microsoft/swin-base-patch4-window7-224,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
facebook/wav2vec2-large-robust,facebook,['arxiv.org/abs/2104.01027']
jjzha/spanbert-base-cased,jjzha,['bibtex']
bertin-project/bertin-roberta-base-spanish,bertin-project,"['arxiv.org/abs/1907.11692', 'bibtex']"
model-attribution-challenge/DialoGPT-large,model-attribution-challenge,['arxiv.org/abs/1911.00536']
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
facebook/wav2vec2-base-100h,facebook,['arxiv.org/abs/2006.11477']
KoboldAI/fairseq-dense-13B,KoboldAI,['arxiv.org/abs/2112.10684']
laion/CLIP-ViT-g-14-laion2B-s12B-b42K,laion,"['arxiv.org/abs/1910.04867', 'doi.org/10.5281/zenodo.5143773}', {'title': 'Learning Transferable Visual Models From Natural Language Supervision'}]"
google/canine-c,google,"['arxiv.org/abs/2103.06874', 'bibtex']"
nghuyong/ernie-health-zh,nghuyong,[{'title': 'Building Chinese Biomedical Language Models via Multi-Level Text Discrimination'}]
mideind/IceBERT,mideind,"['arxiv.org/abs/2201.05601', 'bibtex']"
hustvl/yolos-base,hustvl,"['arxiv.org/abs/2106.00666', 'bibtex']"
microsoft/trocr-base-stage1,microsoft,['arxiv.org/abs/2109.10282']
facebook/deit-tiny-patch16-224,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
Geotrend/distilbert-base-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
speechbrain/tts-tacotron2-ljspeech,speechbrain,['arxiv.org/abs/1712.05884']
allenai/tk-instruct-base-def-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
model-attribution-challenge/distilgpt2,model-attribution-challenge,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
johngiorgi/declutr-small,johngiorgi,"['arxiv.org/abs/2006.03659', {'title': '{D'}]"
sasha/regardv3,sasha,[{'title': 'The woman worked as a babysitter: On biases in language generation'}]
ixa-ehu/SciBERT-SQuAD-QuAC,ixa-ehu,"['arxiv.org/abs/1808.07036', 'bibtex']"
google/bert2bert_L-24_wmt_de_en,google,['arxiv.org/abs/1907.12461']
hfl/chinese-electra-small-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
google/tapas-base-finetuned-wikisql-supervised,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
google/bert_uncased_L-6_H-512_A-8,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
naver-clova-ix/donut-base-finetuned-rvlcdip,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
aubmindlab/bert-base-arabert,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
ixa-ehu/berteus-base-cased,ixa-ehu,[{'title': 'Give your Text Representation Models some Love: the Case for Basque'}]
climatebert/distilroberta-base-climate-f,climatebert,"['arxiv.org/abs/2110.12010', {'title': 'ClimateBERT: A Pretrained Language Model for Climate-Related Text'}]"
ThomasNLG/t5-qg_squad1-en,ThomasNLG,[{'title': 'QuestEval: Summarization Asks for Fact-based Evaluation'}]
google/long-t5-tglobal-xl,google,[{'title': 'LongT5: Efficient Text-To-Text Transformer for Long Sequences'}]
facebook/levit-128S,facebook,['arxiv.org/abs/2104.01136']
microsoft/trocr-small-handwritten,microsoft,['arxiv.org/abs/2109.10282']
sentence-transformers/msmarco-distilroberta-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
asafaya/bert-mini-arabic,asafaya,['bibtex']
Hate-speech-CNERG/bert-base-uncased-hatexplain,Hate-speech-CNERG,[{'title': 'HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection'}]
pierreguillou/gpt2-small-portuguese,pierreguillou,[{'title': 'GPorTuguese-2 (Portuguese GPT-2 small): a Language Model for Portuguese text generation (and more NLP tasks...)'}]
cambridgeltl/BioRedditBERT-uncased,cambridgeltl,['bibtex']
uer/roberta-base-finetuned-jd-full-chinese,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
Helsinki-NLP/opus-mt-tc-big-en-fr,Helsinki-NLP,['bibtex']
monsoon-nlp/bert-base-thai,monsoon-nlp,['arxiv.org/abs/1508.07909']
PlanTL-GOB-ES/gpt2-large-bne,PlanTL-GOB-ES,"['arxiv.org/abs/2107.07253', 'bibtex']"
hfl/chinese-electra-base-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
sentence-transformers/stsb-bert-base,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
relbert/relbert-roberta-large,relbert,['bibtex']
NDugar/ZSD-microsoft-v2xxlmnli,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
google/vit-large-patch16-384,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
allegro/herbert-klej-cased-tokenizer-v1,allegro,['bibtex']
Muennighoff/SGPT-5.8B-weightedmean-msmarco-specb-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
colorfulscoop/sbert-base-ja,colorfulscoop,['arxiv.org/abs/1908.10084']
microsoft/trocr-large-handwritten,microsoft,['arxiv.org/abs/2109.10282']
KES/T5-TTParser,KES,['arxiv.org/abs/1702.04066']
microsoft/layoutlmv3-base-chinese,microsoft,"['arxiv.org/abs/2204.08387', {'title': 'LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking'}]"
Edresson/wav2vec2-large-xlsr-coraa-portuguese,Edresson,['arxiv.org/abs/2110.15731']
model-attribution-challenge/Multilingual-MiniLM-L12-H384,model-attribution-challenge,['arxiv.org/abs/2002.10957']
GanjinZero/coder_all,GanjinZero,"['doi.org/10.1016/j.jbi.2021.103983},', {'title': 'CODER: Knowledge-infused cross-lingual medical term embedding for term normalization'}]"
CAMeL-Lab/bert-base-arabic-camelbert-ca,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
indobenchmark/indobert-lite-base-p1,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
mrm8488/spanbert-finetuned-squadv2,mrm8488,['arxiv.org/abs/1907.10529']
sentence-transformers/msmarco-roberta-base-v3,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
Intel/dpt-large,Intel,"['arxiv.org/abs/2103.13413', 'bibtex']"
deepmind/vision-perceiver-conv,deepmind,"['arxiv.org/abs/2107.14795', 'bibtex']"
nghuyong/ernie-gram-zh,nghuyong,['arxiv.org/abs/2010.12148']
EleutherAI/polyglot-ko-1.3b,EleutherAI,['arxiv.org/abs/2104.09864']
facebook/wav2vec2-base-100k-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
microsoft/tapex-large-finetuned-wtq,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
sagorsarker/bangla-bert-base,sagorsarker,['arxiv.org/abs/1810.04805']
tugstugi/bert-large-mongolian-uncased,tugstugi,['arxiv.org/abs/1810.04805']
zjukg/OntoProtein,zjukg,[{'title': 'OntoProtein: Protein Pretraining With Gene Ontology Embedding'}]
allegro/herbert-klej-cased-v1,allegro,"['arxiv.org/abs/2005.00630', 'bibtex']"
facebook/detr-resnet-101-panoptic,facebook,"['arxiv.org/abs/2005.12872', 'bibtex']"
fnlp/cpt-base,fnlp,[{'title': 'CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation'}]
facebook/deit-base-distilled-patch16-384,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
ThomasNLG/t5-qa_webnlg_synth-en,ThomasNLG,"['arxiv.org/abs/2104.07555', {'title': 'Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation'}]"
csebuetnlp/mT5_m2m_crossSum,csebuetnlp,"['arxiv.org/abs/2112.08804', 'bibtex']"
model-attribution-challenge/xlnet-base-cased,model-attribution-challenge,"['arxiv.org/abs/1906.08237', 'bibtex']"
facebook/data2vec-vision-base,facebook,"['arxiv.org/abs/2202.03555', 'doi.org/10.48550/arxiv.2202.03555,']"
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
setu4993/smaller-LaBSE,setu4993,['arxiv.org/abs/2010.05609']
Hate-speech-CNERG/dehatebert-mono-french,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
microsoft/swin-base-patch4-window12-384-in22k,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
facebook/wav2vec2-xls-r-1b,facebook,['arxiv.org/abs/2111.09296']
MCG-NJU/videomae-base-finetuned-kinetics,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
michiyasunaga/BioLinkBERT-large,michiyasunaga,['arxiv.org/abs/2203.15827']
nvidia/segformer-b4-finetuned-ade-512-512,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
nvidia/segformer-b0-finetuned-cityscapes-768-768,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
Maltehb/aelaectra-danish-electra-small-cased,Maltehb,['arxiv.org/abs/2003.10555']
UBC-NLP/MARBERTv2,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
speechbrain/sepformer-wsj02mix,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
UBC-NLP/AraT5-base,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
ixa-ehu/ixambert-base-cased,ixa-ehu,[{'title': 'Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque'}]
sentence-transformers/msmarco-MiniLM-L-12-v3,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL,microsoft,"['arxiv.org/abs/2112.07887', 'bibtex']"
VietAI/vit5-base,VietAI,['bibtex']
Intel/dpt-large-ade,Intel,"['arxiv.org/abs/2103.13413', 'bibtex']"
junnyu/uer_large,junnyu,[{'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]
xlm-mlm-tlm-xnli15-1024,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
facebook/convnext-xlarge-384-22k-1k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
csebuetnlp/mT5_m2o_chinese_simplified_crossSum,csebuetnlp,"['arxiv.org/abs/2112.08804', 'bibtex']"
cross-encoder/qnli-distilroberta-base,cross-encoder,['arxiv.org/abs/1804.07461']
aubmindlab/aragpt2-base,aubmindlab,"['arxiv.org/abs/2012.15520', 'bibtex']"
TsinghuaAI/CPM-Generate,TsinghuaAI,"['arxiv.org/abs/2012.00413', {'title': 'CPM: A Large-scale Generative Chinese Pre-trained Language Model'}]"
sentence-transformers/bert-large-nli-max-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
mrm8488/bert-tiny-5-finetuned-squadv2,mrm8488,['arxiv.org/abs/1908.08962']
indobenchmark/indobert-large-p1,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
dandelin/vilt-b32-finetuned-coco,dandelin,['arxiv.org/abs/2102.03334']
Harveenchadha/vakyansh-wav2vec2-hindi-him-4200,Harveenchadha,['arxiv.org/abs/2107.07402']
microsoft/prophetnet-large-uncased-squad-qg,microsoft,"['arxiv.org/abs/2001.04063', {'title': 'Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training'}]"
google/vit-base-patch32-384,google,"['arxiv.org/abs/2010.11929', 'doi.org/10.48550/arxiv.2010.11929,', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
AmazonScience/qanlu,AmazonScience,[{'title': 'Language model is all you need: Natural language understanding as question answering'}]
Salesforce/codegen-350M-nl,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
nvidia/segformer-b2-finetuned-cityscapes-1024-1024,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
MLRS/BERTu,MLRS,['bibtex']
facebook/wav2vec2-conformer-rope-large-960h-ft,facebook,['arxiv.org/abs/2010.05171']
cristian-popa/bart-tl-ng,cristian-popa,['bibtex']
fav-kky/FERNET-C5,fav-kky,['arxiv.org/abs/2107.10042']
mrm8488/bert-multi-cased-finetuned-xquadv1,mrm8488,['bibtex']
sentence-transformers/nli-roberta-large,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
google/rembert,google,"['arxiv.org/abs/2010.12821', 'bibtex']"
google/byt5-large,google,['arxiv.org/abs/1907.06292']
nvidia/mit-b4,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
castorini/afriberta_large,castorini,['bibtex']
tscholak/3vnuv1vf,tscholak,"['arxiv.org/abs/2109.05093', 'bibtex']"
google/owlvit-base-patch16,google,"['arxiv.org/abs/2205.06230', {'title': 'Simple Open-Vocabulary Object Detection with Vision Transformers'}]"
GroNLP/gpt2-small-dutch,GroNLP,['arxiv.org/abs/2012.05628']
speechbrain/metricgan-plus-voicebank,speechbrain,[{'title': 'MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement'}]
moussaKam/barthez-orangesum-abstract,moussaKam,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
benjamin/roberta-base-wechsel-german,benjamin,['bibtex']
Geotrend/bert-base-th-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
navteca/bart-large-mnli,navteca,['arxiv.org/abs/1909.00161']
google/t5-small-ssm,google,['arxiv.org/abs/1910.10683']
uer/t5-base-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer'}]"
apanc/russian-sensitive-topics,apanc,"['arxiv.org/abs/2103.05345', 'bibtex']"
eugenesiow/mdsr,eugenesiow,"['arxiv.org/abs/1707.02921', {'title': 'Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network'}]"
Hate-speech-CNERG/dehatebert-mono-english,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
google/vit-huge-patch14-224-in21k,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
tau/splinter-base,tau,['bibtex']
Helsinki-NLP/opus-mt-tc-big-fr-en,Helsinki-NLP,['bibtex']
nguyenvulebinh/envibert,nguyenvulebinh,['bibtex']
google/bert_uncased_L-2_H-768_A-12,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
ccdv/lsg-camembert-base-4096,ccdv,"['doi.org/10.18653%2Fv1%2F2020.acl-main.645},', {'doi': '10.18653/v1/2020.acl-main.645'}]"
superb/wav2vec2-large-superb-ic,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
microsoft/xclip-base-patch16-zero-shot,microsoft,['arxiv.org/abs/2208.02816']
pszemraj/led-large-book-summary,pszemraj,['arxiv.org/abs/2105.08209']
aubmindlab/bert-large-arabertv2,aubmindlab,"['arxiv.org/abs/2003.00104', {'title': 'AraBERT: Transformer-based Model for Arabic Language Understanding'}]"
facebook/convnext-small-224,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
sentence-transformers/nli-bert-base,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
uer/gpt2-distil-chinese-cluecorpussmall,uer,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
apple/deeplabv3-mobilevit-small,apple,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
bvanaken/CORe-clinical-outcome-biobert-v1,bvanaken,['bibtex']
model-attribution-challenge/opt-350m,model-attribution-challenge,['arxiv.org/abs/2205.01068']
facebook/xlm-roberta-xl,facebook,"['arxiv.org/abs/2105.00572', 'bibtex']"
johngiorgi/declutr-base,johngiorgi,"['arxiv.org/abs/2006.03659', {'title': '{D'}]"
sentence-transformers/stsb-bert-large,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
tau/splinter-base-qass,tau,['bibtex']
google/bert_uncased_L-8_H-768_A-12,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
sebastian-hofstaetter/distilbert-dot-margin_mse-T2-msmarco,sebastian-hofstaetter,['arxiv.org/abs/2010.02666']
megagonlabs/transformers-ud-japanese-electra-base-discriminator,megagonlabs,['bibtex']
fnlp/cpt-large,fnlp,[{'title': 'CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation'}]
microsoft/resnet-18,microsoft,['arxiv.org/abs/1512.03385']
mrm8488/spanbert-large-finetuned-squadv1,mrm8488,['arxiv.org/abs/1907.10529']
razent/SciFive-large-Pubmed_PMC,razent,['arxiv.org/abs/2106.03598']
NbAiLab/nb-bert-base-mnli,NbAiLab,['arxiv.org/abs/1909.00161']
speechbrain/m-ctc-t-large,speechbrain,"['arxiv.org/abs/2111.00161', {'title': 'Pseudo-Labeling for Massively Multilingual Speech Recognition'}]"
xlm-mlm-enfr-1024,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
microsoft/swinv2-tiny-patch4-window8-256,microsoft,"['arxiv.org/abs/2111.09883', 'bibtex']"
KoboldAI/GPT-Neo-2.7B-Janeway,KoboldAI,['doi.org/10.5281/zenodo.5297715}']
facebook/maskformer-swin-base-ade,facebook,['arxiv.org/abs/2107.06278']
hfl/chinese-xlnet-mid,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
whaleloops/phrase-bert,whaleloops,"['arxiv.org/abs/2109.06304', 'bibtex']"
KBLab/wav2vec2-large-voxrex-swedish,KBLab,['arxiv.org/abs/2205.03026']
GroNLP/gpt2-small-italian,GroNLP,['arxiv.org/abs/2012.05628']
facebook/convnext-base-224,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
google/ddpm-cifar10-32,google,['arxiv.org/abs/2006.11239']
allenai/tk-instruct-11b-def,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
doc2query/msmarco-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
facebook/xglm-7.5B,facebook,['arxiv.org/abs/2112.10668']
moha/arabert_c19,moha,['arxiv.org/abs/2004.04315']
google/tapas-base-finetuned-tabfact,google,"['arxiv.org/abs/2010.00571', {'title': 'TabFact : A Large-scale Dataset for Table-based Fact Verification'}]"
CAMeL-Lab/bert-base-arabic-camelbert-mix,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
allenai/aspire-biencoder-biomed-spec,allenai,['arxiv.org/abs/2111.08366']
allenai/macaw-11b,allenai,[{'title': 'General-Purpose Question-Answering with {M'}]
facebook/maskformer-swin-tiny-ade,facebook,['arxiv.org/abs/2107.06278']
wannaphong/wav2vec2-large-xlsr-53-th-cv8-newmm,wannaphong,['arxiv.org/abs/2208.04799']
facebook/maskformer-swin-small-coco,facebook,['arxiv.org/abs/2107.06278']
nlpaueb/sec-bert-base,nlpaueb,"['arxiv.org/abs/2203.06482', {'title': 'FiNER: Financial Numeric Entity Recognition for XBRL Tagging'}]"
cimm-kzn/endr-bert,cimm-kzn,"['arxiv.org/abs/2004.03659', 'doi.org/10.1093/bioinformatics/btaa675},', 'bibtex']"
google/bert_uncased_L-6_H-256_A-4,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
ThomasNLG/t5-qg_webnlg_synth-en,ThomasNLG,"['arxiv.org/abs/2104.07555', {'title': 'Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation'}]"
uw-hai/polyjuice,uw-hai,['bibtex']
superb/wav2vec2-base-superb-ks,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
allenai/tk-instruct-large-def-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
finiteautomata/beto-emotion-analysis,finiteautomata,"['arxiv.org/abs/2106.09462', {'title': 'EmoEvent: A multilingual emotion corpus based on different events'}]"
xlm-mlm-ende-1024,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
eleldar/language-detection,eleldar,['arxiv.org/abs/1911.02116']
unicamp-dl/mMiniLM-L6-v2-mmarco-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
dandelin/vilt-b32-mlm-itm,dandelin,['arxiv.org/abs/2102.03334']
tomh/toxigen_roberta,tomh,"['arxiv.org/abs/2203.09509', 'bibtex']"
readerbench/RoBERT-large,readerbench,[{'title': 'RoBERT--A Romanian BERT Model'}]
cimm-kzn/enrudr-bert,cimm-kzn,"['arxiv.org/abs/2004.03659', 'doi.org/10.1093/bioinformatics/btaa675},', 'bibtex']"
sijunhe/nezha-cn-base,sijunhe,['arxiv.org/abs/1909.00204']
dbmdz/convbert-base-turkish-cased,dbmdz,['arxiv.org/abs/2008.02496']
google/tapas-base-finetuned-sqa,google,['arxiv.org/abs/2004.02349']
csebuetnlp/banglat5,csebuetnlp,"['arxiv.org/abs/2205.11081', 'bibtex']"
facebook/deit-tiny-distilled-patch16-224,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
speechbrain/lang-id-voxlingua107-ecapa,speechbrain,[{'title': '{VoxLingua107'}]
CAMeL-Lab/bert-base-arabic-camelbert-msa,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
facebook/genre-linking-aidayago2,facebook,"['arxiv.org/abs/2010.00904', {'title': 'Autoregressive Entity Retrieval'}]"
facebook/xglm-2.9B,facebook,['arxiv.org/abs/2112.10668']
alibaba-pai/pai-dkplm-medical-base-zh,alibaba-pai,"['arxiv.org/abs/2205.00258', {'title': 'EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing'}]"
jcblaise/bert-tagalog-base-uncased,jcblaise,[{'title': 'Establishing Baselines for Text Classification in Low-Resource Languages'}]
uer/roberta-base-finetuned-jd-binary-chinese,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
Muennighoff/SGPT-125M-weightedmean-nli-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
sentence-transformers/gtr-t5-large,sentence-transformers,['arxiv.org/abs/2112.07899']
nvidia/segformer-b4-finetuned-cityscapes-1024-1024,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
PlanTL-GOB-ES/roberta-large-bne-sqac,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
microsoft/xclip-base-patch32,microsoft,['arxiv.org/abs/2208.02816']
Huffon/sentence-klue-roberta-base,Huffon,['arxiv.org/abs/1908.10084']
bvanaken/CORe-clinical-diagnosis-prediction,bvanaken,['bibtex']
microsoft/unispeech-sat-base-plus-sv,microsoft,['arxiv.org/abs/1912.07875']
aneuraz/awesome-align-with-co,aneuraz,"['arxiv.org/abs/2101.08231', {'title': 'Word Alignment by Fine-tuning Embeddings on Parallel Corpora'}]"
moussaKam/barthez-orangesum-title,moussaKam,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
VietAI/vit5-large-vietnews-summarization,VietAI,['bibtex']
Hate-speech-CNERG/dehatebert-mono-spanish,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
uer/gpt2-chinese-ancient,uer,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
Salesforce/mixqg-base,Salesforce,['arxiv.org/abs/2110.08175']
superb/hubert-large-superb-er,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
flair/upos-multi,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
sentence-transformers/msmarco-MiniLM-L6-cos-v5,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
DeepPavlov/bert-base-multilingual-cased-sentence,DeepPavlov,['arxiv.org/abs/1704.05426']
indobenchmark/indobart,indobenchmark,"['arxiv.org/abs/2104.08200', {'title': 'IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation'}]"
hfl/chinese-electra-large-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
deepmind/vision-perceiver-fourier,deepmind,"['arxiv.org/abs/2107.14795', 'bibtex']"
allegro/plt5-small,allegro,[{'title': 'Evaluation of Transfer Learning for Polish with a Text-to-Text Model'}]
GroNLP/gpt2-medium-italian-embeddings,GroNLP,['arxiv.org/abs/2012.05628']
sentence-transformers/bert-large-nli-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
stanford-oval/paraphraser-bart-large,stanford-oval,"['arxiv.org/abs/2010.04806', 'bibtex']"
allegro/plt5-base,allegro,[{'title': 'Evaluation of Transfer Learning for Polish with a Text-to-Text Model'}]
flaubert/flaubert_large_cased,flaubert,[{'title': 'FlauBERT: des mod{\\`e'}]
UBC-NLP/AraT5-base-title-generation,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
dalle-mini/dalle-mega,dalle-mini,['arxiv.org/abs/1910.09700']
speechbrain/mtl-mimic-voicebank,speechbrain,[{'title': 'Spectral Feature Mapping with Mimic Loss for Robust Speech Recognition'}]
Helsinki-NLP/opus-mt-tc-big-fi-en,Helsinki-NLP,['bibtex']
facebook/data2vec-vision-base-ft1k,facebook,"['arxiv.org/abs/2202.03555', 'doi.org/10.48550/arxiv.2202.03555,']"
IIC/dpr-spanish-passage_encoder-allqa-base,IIC,['arxiv.org/abs/2004.04906']
gchhablani/bert-base-cased-finetuned-sst2,gchhablani,['arxiv.org/abs/2105.03824']
facebook/dino-vitb8,facebook,"['arxiv.org/abs/2010.11929', 'bibtex']"
allenai/aspire-contextualsentence-singlem-compsci,allenai,['arxiv.org/abs/2111.08366']
imthanhlv/gpt2news,imthanhlv,['bibtex']
IIC/dpr-spanish-question_encoder-allqa-base,IIC,['arxiv.org/abs/2004.04906']
nvidia/groupvit-gcc-yfcc,nvidia,"['arxiv.org/abs/2202.11094', 'bibtex']"
rinna/japanese-cloob-vit-b-16,rinna,['arxiv.org/abs/2110.11316']
EMBEDDIA/finest-bert,EMBEDDIA,"['arxiv.org/abs/2006.07890', 'doi.org/10.1007/978-3-030-58323-1_11"",']"
razent/spbert-mlm-wso-base,razent,['arxiv.org/abs/2106.09997']
Visual-Attention-Network/van-base,Visual-Attention-Network,['arxiv.org/abs/2202.09741']
studio-ousia/mluke-base,studio-ousia,[{'title': 'mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models'}]
model-attribution-challenge/bloom-350m,model-attribution-challenge,['arxiv.org/abs/1909.08053']
vinvino02/glpn-nyu,vinvino02,"['arxiv.org/abs/2201.07436', 'bibtex']"
daveni/twitter-xlm-roberta-emotion-es,daveni,[{'title': 'GSI-UPM at IberLEF2021: Emotion Analysis of Spanish Tweets by Fine-tuning the XLM-RoBERTa Language Model'}]
facebook/regnet-y-040,facebook,['arxiv.org/abs/2003.13678']
microsoft/unispeech-sat-base-100h-libri-ft,microsoft,['arxiv.org/abs/2110.05752']
pucpr/clinicalnerpt-chemical,pucpr,['bibtex']
jpwahle/t5-word-sense-disambiguation,jpwahle,[{'title': 'Incorporating Word Sense Disambiguation in Neural Language Models'}]
sentence-transformers/nli-distilroberta-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/wavlm-base,microsoft,['arxiv.org/abs/2110.13900']
fbaigt/procbert,fbaigt,"['arxiv.org/abs/2109.04711', 'bibtex']"
sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
nvidia/segformer-b3-finetuned-ade-512-512,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
google/vit-large-patch16-224-in21k,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
Xuhui/ToxDect-roberta-large,Xuhui,[{'title': 'Challenges in Automated Debiasing for Toxic Language Detection'}]
flair/upos-english-fast,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
razent/SciFive-base-Pubmed_PMC,razent,['arxiv.org/abs/2106.03598']
microsoft/xprophetnet-large-wiki100-cased,microsoft,"['arxiv.org/abs/2001.04063', {'title': 'Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training'}]"
UWB-AIR/Czert-B-base-cased,UWB-AIR,"['arxiv.org/abs/2103.13031', {'title': 'Czert -- Czech BERT-like Model for Language Representation'}]"
CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
KoboldAI/fairseq-dense-1.3B,KoboldAI,['arxiv.org/abs/2112.10684']
indobenchmark/indobert-large-p2,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
mrm8488/CodeBERTaPy,mrm8488,"['arxiv.org/abs/1909.09436', {'title': '{CodeSearchNet'}]"
unicamp-dl/translation-pt-en-t5,unicamp-dl,['bibtex']
sentence-transformers/bert-base-nli-max-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
flax-sentence-embeddings/all_datasets_v3_roberta-large,flax-sentence-embeddings,"['arxiv.org/abs/1810.09305', 'doi.org/10.18653/v1/d15-1075),']"
mideind/yfirlestur-icelandic-correction-byt5,mideind,['arxiv.org/abs/2105.13626']
Salesforce/codet5-large-ntp-py,Salesforce,"['arxiv.org/abs/1909.09436', 'bibtex']"
facebook/xglm-4.5B,facebook,['arxiv.org/abs/2112.10668']
naver/efficient-splade-VI-BT-large-doc,naver,"['doi.org/10.1145/3477495.3531833},', 'bibtex']"
mrm8488/codeBERTaJS,mrm8488,"['arxiv.org/abs/1909.09436', {'title': '{CodeSearchNet'}]"
facebook/wav2vec2-lv-60-espeak-cv-ft,facebook,['arxiv.org/abs/2109.11680']
Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
google/vit-large-patch16-224,google,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
razent/SciFive-large-Pubmed_PMC-MedNLI,razent,['arxiv.org/abs/2106.03598']
naver/efficient-splade-VI-BT-large-query,naver,"['doi.org/10.1145/3477495.3531833},', 'bibtex']"
nickmuchi/yolos-small-rego-plates-detection,nickmuchi,['arxiv.org/abs/2106.00666']
uer/chinese_roberta_L-6_H-128,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
CompVis/ldm-celebahq-256,CompVis,['arxiv.org/abs/2112.10752']
allenai/wmt19-de-en-6-6-base,allenai,['arxiv.org/abs/2006.10369']
alger-ia/dziribert,alger-ia,[{'title': 'DziriBERT: a Pre-trained Language Model for the Algerian Dialect'}]
HYPJUDY/layoutlmv3-large-finetuned-funsd,HYPJUDY,[{'title': 'LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking'}]
cmarkea/distilcamembert-base-nli,cmarkea,['bibtex']
csebuetnlp/banglabert_generator,csebuetnlp,"['arxiv.org/abs/2101.00204', {'title': 'BanglaBERT: Lagnuage Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla'}]"
lordtt13/emo-mobilebert,lordtt13,['arxiv.org/abs/2004.02984']
speechbrain/lang-id-commonlanguage_ecapa,speechbrain,['bibtex']
facebook/wav2vec2-large-100k-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
gsarti/it5-base,gsarti,"['arxiv.org/abs/2203.03759', {'title': 'IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation'}]"
model-attribution-challenge/codegen-350M-multi,model-attribution-challenge,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
PlanTL-GOB-ES/roberta-base-biomedical-es,PlanTL-GOB-ES,['arxiv.org/abs/2109.03570']
google/t5-efficient-xxl,google,['arxiv.org/abs/2109.10686']
PlanTL-GOB-ES/roberta-base-ca,PlanTL-GOB-ES,"['doi.org/10.5281/zenodo.4762030)', 'bibtex']"
bhadresh-savani/roberta-base-emotion,bhadresh-savani,['arxiv.org/abs/1907.11692']
facebook/s2t-wav2vec2-large-en-de,facebook,"['arxiv.org/abs/2104.06678', 'bibtex']"
pszemraj/bigbird-pegasus-large-K-booksum,pszemraj,['arxiv.org/abs/2105.08209']
sentence-transformers/sentence-t5-large,sentence-transformers,['arxiv.org/abs/2108.08877']
DeepPavlov/distilrubert-small-cased-conversational,DeepPavlov,"['arxiv.org/abs/2205.02340', 'doi.org/10.48550/arxiv.2205.02340,']"
google/bert_uncased_L-12_H-128_A-2,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
aliosm/ComVE-distilgpt2,aliosm,[{'title': 'JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation'}]
sentence-transformers/bert-large-nli-stsb-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
paust/pko-t5-base,paust,['arxiv.org/abs/2105.09680']
naver/efficient-splade-V-large-query,naver,"['doi.org/10.1145/3477495.3531833},', 'bibtex']"
HamidRezaAttar/gpt2-product-description-generator,HamidRezaAttar,['arxiv.org/abs/1706.03762']
speechbrain/slu-timers-and-such-direct-librispeech-asr,speechbrain,"['arxiv.org/abs/2104.01604', {'title': '{Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers'}]"
facebook/convnext-large-224-22k-1k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
pszemraj/grammar-synthesis-large,pszemraj,['arxiv.org/abs/2107.06751']
flair/ner-german-legal,flair,['bibtex']
asapp/sew-tiny-100k,asapp,['arxiv.org/abs/2109.06870']
nvidia/segformer-b1-finetuned-cityscapes-1024-1024,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
microsoft/trocr-small-stage1,microsoft,['arxiv.org/abs/2109.10282']
Intel/bert-base-uncased-sparse-90-unstructured-pruneofa,Intel,['arxiv.org/abs/2111.05754']
uer/bart-large-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension'}]"
ccdv/lsg-albert-base-v2-4096,ccdv,"['arxiv.org/abs/1909.11942', 'bibtex']"
microsoft/swin-base-patch4-window12-384,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
superb/wav2vec2-base-superb-sid,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
KoboldAI/GPT-Neo-2.7B-Picard,KoboldAI,['doi.org/10.5281/zenodo.5297715}']
Helsinki-NLP/opus-mt-tc-big-en-hu,Helsinki-NLP,['bibtex']
tscholak/1wnr382e,tscholak,"['arxiv.org/abs/2109.05093', 'bibtex']"
RUCAIBox/mvp-data-to-text,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
razent/cotext-1-ccg,razent,['bibtex']
Team-PIXEL/pixel-base,Team-PIXEL,"['arxiv.org/abs/2207.06991', {'title': 'Language Modelling with Pixels'}]"
sentence-transformers/quora-distilbert-base,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/wavlm-base-plus-sd,microsoft,['arxiv.org/abs/1912.07875']
mio/Artoria,mio,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
allenai/tk-instruct-3b-def,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
dalle-mini/dalle-mini,dalle-mini,['arxiv.org/abs/2102.08981']
bigscience/distill-bloom-1b3,bigscience,['arxiv.org/abs/1909.08053']
KoboldAI/GPT-J-6B-Janeway,KoboldAI,['arxiv.org/abs/2101.00027']
readerbench/jurBERT-base,readerbench,[{'title': 'jurBERT: A Romanian BERT Model for Legal Judgement Prediction'}]
vinvino02/glpn-kitti,vinvino02,"['arxiv.org/abs/2201.07436', 'bibtex']"
sberbank-ai/mGPT-armenian,sberbank-ai,"['arxiv.org/abs/2204.07580', 'doi.org/10.48550/arxiv.2204.07580,']"
jcblaise/roberta-tagalog-large,jcblaise,[{'title': 'Improving Large-scale Language Models and Resources for Filipino'}]
DTAI-KULeuven/robbert-v2-dutch-sentiment,DTAI-KULeuven,['bibtex']
nvidia/segformer-b0-finetuned-cityscapes-1024-1024,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
camembert/camembert-base-ccnet,camembert,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
HiTZ/A2T_RoBERTa_SMFA_TACRED-re,HiTZ,"['arxiv.org/abs/2104.14690', 'bibtex']"
flair/upos-multi-fast,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
racai/distilbert-base-romanian-uncased,racai,"['arxiv.org/abs/2112.12650', {'title': 'Distilling the Knowledge of Romanian BERTs Using Multiple Teachers'}]"
l3cube-pune/hing-bert,l3cube-pune,['arxiv.org/abs/2204.08398']
pszemraj/grammar-synthesis-base,pszemraj,['arxiv.org/abs/2107.06751']
CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
facebook/wav2vec2-conformer-rel-pos-large-960h-ft,facebook,['arxiv.org/abs/2010.05171']
SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune,SEBIS,['arxiv.org/abs/1910.09700']
CAMeL-Lab/bert-base-arabic-camelbert-da,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
juancopi81/mutopia_guitar_mmm,juancopi81,['arxiv.org/abs/2008.06048']
jcblaise/bert-tagalog-base-cased,jcblaise,[{'title': 'Establishing Baselines for Text Classification in Low-Resource Languages'}]
sentence-transformers/distilbert-base-nli-stsb-quora-ranking,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
akhooli/gpt2-small-arabic,akhooli,['bibtex']
facebook/wav2vec2-conformer-rel-pos-large,facebook,['arxiv.org/abs/2010.05171']
razent/spbert-mlm-base,razent,['arxiv.org/abs/2106.09997']
model-attribution-challenge/bloom-2b5,model-attribution-challenge,['arxiv.org/abs/1909.08053']
microsoft/tapex-base,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
phiyodr/bart-large-finetuned-squad2,phiyodr,['arxiv.org/abs/1806.03822']
jcblaise/roberta-tagalog-base,jcblaise,[{'title': 'Improving Large-scale Language Models and Resources for Filipino'}]
thu-coai/CDial-GPT_LCCC-large,thu-coai,['arxiv.org/abs/1901.08149']
espnet/GunnarThor_talromur_b_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/roberta2roberta_L-24_bbc,google,['arxiv.org/abs/1907.12461']
sismetanin/rubert-ru-sentiment-rusentiment,sismetanin,['bibtex']
izumi-lab/electra-small-japanese-fin-discriminator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
nvidia/stt_fr_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
sentence-transformers/roberta-base-nli-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
ccdv/lsg-xlm-roberta-base-4096,ccdv,"['arxiv.org/abs/2105.00572', 'bibtex']"
microsoft/beit-large-patch16-224-pt22k-ft22k,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Hate-speech-CNERG/dehatebert-mono-portugese,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
microsoft/beit-large-finetuned-ade-640-640,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-msa-ner,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
UBC-NLP/AraT5-msa-base,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
obrizum/all-MiniLM-L6-v2,obrizum,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
jcblaise/electra-tagalog-small-uncased-discriminator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
speechbrain/urbansound8k_ecapa,speechbrain,['bibtex']
HooshvareLab/bert-base-parsbert-armanner-uncased,HooshvareLab,"['arxiv.org/abs/2005.12515', {'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]"
pucpr/clinicalnerpt-procedure,pucpr,['bibtex']
MCG-NJU/videomae-base-ssv2,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
edbeeching/decision-transformer-gym-hopper-expert,edbeeching,['arxiv.org/abs/2106.01345']
asafaya/bert-large-arabic,asafaya,['bibtex']
jpwahle/longformer-base-plagiarism-detection,jpwahle,['doi.org/10.5281/zenodo.3608000)']
facebook/m2m100-12B-avg-5-ckpt,facebook,['arxiv.org/abs/2010.11125']
espnet/english_male_ryanspeech_fastspeech2,espnet,[{'title': 'RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis'}]
neuropark/sahajBERT,neuropark,"['arxiv.org/abs/1909.11942', 'bibtex']"
HooshvareLab/bert-base-parsbert-ner-uncased,HooshvareLab,"['arxiv.org/abs/2005.12515', {'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]"
optimum/all-MiniLM-L6-v2,optimum,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
nghuyong/ernie-3.0-nano-zh,nghuyong,"['arxiv.org/abs/2107.02137', {'title': 'Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation'}]"
pucpr/biobertpt-all,pucpr,['bibtex']
paust/pko-t5-large,paust,['arxiv.org/abs/2105.09680']
nlpaueb/sec-bert-shape,nlpaueb,"['arxiv.org/abs/2203.06482', {'title': 'FiNER: Financial Numeric Entity Recognition for XBRL Tagging'}]"
tner/roberta-large-ontonotes5,tner,['bibtex']
KoboldAI/fairseq-dense-355M,KoboldAI,['arxiv.org/abs/2112.10684']
allenai/wmt19-de-en-6-6-big,allenai,['arxiv.org/abs/2006.10369']
RUCAIBox/mtl-data-to-text,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
relbert/roberta-large-conceptnet-average-prompt-c-nce,relbert,['bibtex']
DTAI-KULeuven/robbertje-1-gb-non-shuffled,DTAI-KULeuven,['arxiv.org/abs/2101.05716']
uer/t5-v1_1-small-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer'}]"
relbert/roberta-large-conceptnet-average-prompt-a-nce,relbert,['bibtex']
uer/pegasus-base-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Pegasus: Pre-training with extracted gap-sentences for abstractive summarization'}]"
tner/deberta-v3-large-ontonotes5,tner,['bibtex']
projecte-aina/roberta-base-ca-v2,projecte-aina,"['doi.org/10.5281/zenodo.4762030)', 'bibtex']"
indobenchmark/indobart-v2,indobenchmark,"['arxiv.org/abs/2104.08200', {'title': 'IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation'}]"
relbert/roberta-large-conceptnet-mask-prompt-e-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-a-nce,relbert,['bibtex']
microsoft/prophetnet-large-uncased-cnndm,microsoft,"['arxiv.org/abs/2001.04063', {'title': 'Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training'}]"
relbert/roberta-large-conceptnet-average-prompt-b-nce,relbert,['bibtex']
relbert/roberta-large-conceptnet-mask-prompt-c-nce,relbert,['bibtex']
facebook/hubert-xlarge-ll60k,facebook,['arxiv.org/abs/2106.07447']
xlm-mlm-xnli15-1024,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
microsoft/swin-small-patch4-window7-224,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
tscholak/2e826ioa,tscholak,"['arxiv.org/abs/2109.05093', 'bibtex']"
facebook/convnext-xlarge-224-22k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
relbert/roberta-large-conceptnet-mask-prompt-d-nce,relbert,['bibtex']
aubmindlab/aragpt2-mega,aubmindlab,"['arxiv.org/abs/2012.15520', 'bibtex']"
jcblaise/electra-tagalog-base-cased-discriminator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
mrm8488/spanbert-large-finetuned-squadv2,mrm8488,['arxiv.org/abs/1907.10529']
uer/gpt2-chinese-couplet,uer,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
doc2query/all-with_prefix-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
kanishka/GlossBERT,kanishka,['bibtex']
microsoft/resnet-152,microsoft,"['arxiv.org/abs/1512.03385', {'title': 'Deep residual learning for image recognition'}]"
openai/imagegpt-small,openai,[{'title': 'Imagenet: A large-scale hierarchical image database'}]
flair/chunk-english-fast,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-e-loob-conceptnet-validated,relbert,['bibtex']
LeBenchmark/wav2vec2-FR-7K-base,LeBenchmark,[{'title': 'LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech'}]
relbert/roberta-large-conceptnet-average-prompt-d-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-c-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-c-loob-conceptnet-validated,relbert,['bibtex']
VietAI/vit5-large,VietAI,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-b-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-e-loob-conceptnet-validated,relbert,['bibtex']
alisawuffles/roberta-large-wanli,alisawuffles,['arxiv.org/abs/2201.05955']
relbert/roberta-large-semeval2012-mask-prompt-a-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-conceptnet-mask-prompt-b-nce,relbert,['bibtex']
relbert/roberta-large-conceptnet-mask-prompt-a-nce,relbert,['bibtex']
relbert/roberta-large-conceptnet-average-prompt-e-nce,relbert,['bibtex']
sentence-transformers/gtr-t5-xl,sentence-transformers,['arxiv.org/abs/2112.07899']
albert-large-v1,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
Narrativa/mT5-base-finetuned-tydiQA-question-generation,Narrativa,['arxiv.org/abs/2010.11934']
uer/chinese_roberta_L-8_H-256,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
facebook/s2t-small-covost2-ca-en-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
johngiorgi/declutr-sci-base,johngiorgi,"['arxiv.org/abs/2006.03659', {'title': '{D'}]"
minwhoo/bart-base-negative-claim-generation,minwhoo,"['arxiv.org/abs/2109.15107', {'title': 'CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models'}]"
csebuetnlp/mT5_m2o_english_crossSum,csebuetnlp,"['arxiv.org/abs/2112.08804', 'bibtex']"
csebuetnlp/mT5_m2o_hindi_crossSum,csebuetnlp,"['arxiv.org/abs/2112.08804', 'bibtex']"
biu-nlp/lingmess-coref,biu-nlp,"['arxiv.org/abs/2205.12644', 'doi.org/10.48550/arxiv.2205.12644,']"
aubmindlab/aragpt2-medium,aubmindlab,"['arxiv.org/abs/2012.15520', 'bibtex']"
microsoft/tapex-base-finetuned-wtq,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
sentence-transformers/gtr-t5-base,sentence-transformers,['arxiv.org/abs/2112.07899']
relbert/roberta-large-semeval2012-mask-prompt-a-nce-classification,relbert,['bibtex']
l3cube-pune/marathi-ner,l3cube-pune,['arxiv.org/abs/2204.06029']
sismetanin/rubert-toxic-pikabu-2ch,sismetanin,['doi.org/10.28995/2075-7182-2020-19-1149-1159).']
lanwuwei/GigaBERT-v3-Arabic-and-English,lanwuwei,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification,relbert,['bibtex']
sail/poolformer_s12,sail,"['arxiv.org/abs/2111.11418', {'title': 'MetaFormer is Actually What You Need for Vision'}]"
google/roberta2roberta_L-24_cnn_daily_mail,google,['arxiv.org/abs/1907.12461']
relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification,relbert,['bibtex']
SenseTime/deformable-detr,SenseTime,"['arxiv.org/abs/2010.04159', 'doi.org/10.48550/arxiv.2010.04159,']"
cardiffnlp/xlm-twitter-politics-sentiment,cardiffnlp,['arxiv.org/abs/2104.12250']
PlanTL-GOB-ES/gpt2-base-bne,PlanTL-GOB-ES,"['arxiv.org/abs/2107.07253', 'bibtex']"
JDBN/t5-base-fr-qg-fquad,JDBN,[{'title': 'Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer'}]
asapp/sew-tiny-100k-ft-ls100h,asapp,['arxiv.org/abs/2109.06870']
relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-d-nce,relbert,['bibtex']
PlanTL-GOB-ES/roberta-large-bne-capitel-ner,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
relbert/roberta-large-semeval2012-average-prompt-b-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-d-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-e-nce-classification,relbert,['bibtex']
abhilash1910/financial_roberta,abhilash1910,['arxiv.org/abs/1907.11692']
relbert/roberta-large-semeval2012-mask-prompt-b-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-c-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-c-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-a-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-e-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-e-nce,relbert,['bibtex']
asapp/sew-d-tiny-100k-ft-ls100h,asapp,['arxiv.org/abs/2109.06870']
facebook/wav2vec2-large,facebook,['arxiv.org/abs/2006.11477']
allegro/plt5-large,allegro,[{'title': 'Evaluation of Transfer Learning for Polish with a Text-to-Text Model'}]
asi/gpt-fr-cased-small,asi,['bibtex']
nvidia/segformer-b3-finetuned-cityscapes-1024-1024,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
google/tapas-small-finetuned-wtq,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
relbert/roberta-large-semeval2012-mask-prompt-c-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-average-prompt-d-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-c-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-b-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-d-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-a-loob,relbert,['bibtex']
google/bert_uncased_L-12_H-256_A-4,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
relbert/roberta-large-semeval2012-mask-prompt-d-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-e-loob,relbert,['bibtex']
naclbit/gpt-j-japanese-6.8b,naclbit,['arxiv.org/abs/2104.09864']
relbert/roberta-large-semeval2012-v2-average-prompt-e-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-mask-prompt-a-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-d-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-c-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-c-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-mask-prompt-b-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-e-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-a-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-average-prompt-b-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-e-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-b-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-d-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-e-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-e-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-b-nce-classification-conceptnet-validated,relbert,['bibtex']
facebook/s2t-medium-mustc-multilingual-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
relbert/roberta-large-semeval2012-v2-average-prompt-c-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-e-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-a-nce-classification,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-mask-prompt-e-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-a-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-mask-prompt-c-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-average-prompt-a-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-e-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-b-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-a-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-d-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-mask-prompt-d-nce,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-a-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-b-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-c-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-b-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-d-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-c-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-b-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-a-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-c-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-mask-prompt-a-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-e-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-d-loob,relbert,['bibtex']
l3cube-pune/marathi-bert-v2,l3cube-pune,['arxiv.org/abs/2202.01159']
xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing,xfbai,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-b-nce-classification,relbert,['bibtex']
paust/pko-t5-small,paust,['arxiv.org/abs/2105.09680']
Davlan/bert-base-multilingual-cased-masakhaner,Davlan,"['arxiv.org/abs/2103.11811', {'title': 'Masakha{NER'}]"
indobenchmark/indobert-lite-base-p2,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
relbert/roberta-large-semeval2012-average-prompt-c-nce-classification,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-prompt-d-nce-classification,relbert,['bibtex']
microsoft/tapex-large,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
jcblaise/electra-tagalog-base-uncased-discriminator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
nvidia/segformer-b1-finetuned-ade-512-512,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
HooshvareLab/bert-fa-base-uncased-clf-persiannews,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
naver/efficient-splade-V-large-doc,naver,"['doi.org/10.1145/3477495.3531833},', 'bibtex']"
relbert/roberta-large-semeval2012-average-prompt-e-nce-classification,relbert,['bibtex']
microsoft/resnet-34,microsoft,"['arxiv.org/abs/1512.03385', {'title': 'Deep residual learning for image recognition'}]"
HYPJUDY/layoutlmv3-base-finetuned-funsd,HYPJUDY,[{'title': 'LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking'}]
pucpr/clinicalnerpt-pharmacologic,pucpr,['bibtex']
apple/deeplabv3-mobilevit-xx-small,apple,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
AdapterHub/bert-base-uncased-pf-snli,AdapterHub,['bibtex']
pucpr/clinicalnerpt-quantitative,pucpr,['bibtex']
sentence-transformers/nli-roberta-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
hfl/chinese-electra-180g-large-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
Finnish-NLP/roberta-large-finnish-v2,Finnish-NLP,['arxiv.org/abs/1907.11692']
facebook/tts_transformer-es-css10,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
monilouise/ner_news_portuguese,monilouise,"['arxiv.org/abs/1909.10649', 'bibtex']"
microsoft/beit-large-patch16-384,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
BSC-TeMU/roberta-base-biomedical-clinical-es,BSC-TeMU,['arxiv.org/abs/2109.03570']
microsoft/dit-large,microsoft,"['arxiv.org/abs/2203.02378', {'title': 'Building a test collection for complex document information processing'}]"
google/roberta2roberta_L-24_discofuse,google,['arxiv.org/abs/1907.12461']
ai4bharat/MultiIndicSentenceSummarization,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
tsantos/PathologyBERT,tsantos,['arxiv.org/abs/1903.10676']
facebook/dino-vits8,facebook,"['arxiv.org/abs/2010.11929', 'bibtex']"
speechbrain/sepformer-whamr-enhancement,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
Helsinki-NLP/opus-mt-tc-big-hu-en,Helsinki-NLP,['bibtex']
jakelever/coronabert,jakelever,['doi.org/10.1101/2020.12.21.423860)']
VietAI/vit5-base-vietnews-summarization,VietAI,['bibtex']
hfl/chinese-legal-electra-base-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
facebook/tts_transformer-zh-cv7_css10,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
facebook/xglm-1.7B,facebook,['arxiv.org/abs/2112.10668']
Jiva/xlm-roberta-large-it-mnli,Jiva,['arxiv.org/abs/1911.02116']
michiyasunaga/LinkBERT-base,michiyasunaga,['arxiv.org/abs/2203.15827']
Salesforce/codegen-16B-nl,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
facebook/wmt21-dense-24-wide-en-x,facebook,"['arxiv.org/abs/2108.03265', {'title': 'Facebook AI’s WMT21 News Translation Task Submission'}]"
yanaiela/roberta-base-epoch_83,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
xlm-mlm-17-1280,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
debatelab/argument-analyst,debatelab,['arxiv.org/abs/2110.01509']
Finnish-NLP/wav2vec2-large-uralic-voxpopuli-v2-finnish,Finnish-NLP,['arxiv.org/abs/2006.11477']
cristian-popa/bart-tl-all,cristian-popa,['bibtex']
malteos/scincl,malteos,['arxiv.org/abs/2202.06671']
aubmindlab/aragpt2-large,aubmindlab,"['arxiv.org/abs/2012.15520', 'bibtex']"
bertin-project/bertin-gpt-j-6B,bertin-project,"['arxiv.org/abs/2104.09864', 'bibtex']"
facebook/regnet-y-320-seer,facebook,['arxiv.org/abs/2202.08360']
w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0,w11wo,['arxiv.org/abs/1907.11692']
ai4bharat/indicwav2vec-hindi,ai4bharat,['arxiv.org/abs/2006.11477']
yikuan8/Clinical-BigBird,yikuan8,[{'title': 'Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences'}]
microsoft/xclip-large-patch14,microsoft,['arxiv.org/abs/2208.02816']
HooshvareLab/bert-fa-base-uncased-sentiment-snappfood,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
google/bert_uncased_L-12_H-512_A-8,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
espnet/GunnarThor_talromur_a_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
edwardjross/xlm-roberta-base-finetuned-recipe-all,edwardjross,['arxiv.org/abs/2004.12184']
Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned,Finnish-NLP,['arxiv.org/abs/2006.11477']
superb/wav2vec2-base-superb-er,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
sahri/indonesiasentiment,sahri,['arxiv.org/abs/1907.11692']
Salesforce/codegen-2B-nl,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
microsoft/unispeech-large-1500h-cv,microsoft,['arxiv.org/abs/2101.07597']
Salesforce/codegen-6B-nl,Salesforce,"['arxiv.org/abs/2203.13474', {'title': 'A Conversational Paradigm for Program Synthesis'}]"
EleutherAI/polyglot-ko-3.8b,EleutherAI,['arxiv.org/abs/2104.09864']
sentence-transformers/sentence-t5-xxl,sentence-transformers,['arxiv.org/abs/2108.08877']
laituan245/molt5-small,laituan245,['arxiv.org/abs/2204.11817']
iarfmoose/roberta-small-bulgarian-pos,iarfmoose,['arxiv.org/abs/1907.11692']
rohanrajpal/bert-base-codemixed-uncased-sentiment,rohanrajpal,['bibtex']
digitalepidemiologylab/covid-twitter-bert-v2-mnli,digitalepidemiologylab,"['arxiv.org/abs/1909.00161', {'title': 'COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter'}]"
hyesunyun/update-summarization-bart-large-longformer,hyesunyun,['bibtex']
QCRI/PropagandaTechniquesAnalysis-en-BERT,QCRI,['bibtex']
sarnikowski/convbert-medium-small-da-cased,sarnikowski,['arxiv.org/abs/2008.02496']
ethzanalytics/ai-msgbot-gpt2-XL,ethzanalytics,['bibtex']
dandelin/vilt-b32-finetuned-nlvr2,dandelin,['arxiv.org/abs/2102.03334']
nvidia/segformer-b0-finetuned-cityscapes-512-1024,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
nvidia/segformer-b2-finetuned-ade-512-512,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
Hate-speech-CNERG/dehatebert-mono-arabic,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
facebook/dino-vits16,facebook,"['arxiv.org/abs/2010.11929', 'bibtex']"
facebook/s2t-small-covost2-es-en-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
ybelkada/t5-11b-sharded,ybelkada,"['arxiv.org/abs/1805.12471', 'bibtex']"
ml6team/keyphrase-extraction-kbir-openkp,ml6team,['arxiv.org/abs/2112.08547']
asapp/sew-d-tiny-100k,asapp,['arxiv.org/abs/2109.06870']
apple/mobilevit-xx-small,apple,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
DeepPavlov/distilrubert-tiny-cased-conversational-5k,DeepPavlov,"['arxiv.org/abs/2205.02340', 'doi.org/10.48550/arxiv.2205.02340,']"
flax-community/gpt2-bengali,flax-community,['bibtex']
Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2,Finnish-NLP,['arxiv.org/abs/2111.09296']
sentence-transformers/sentence-t5-xl,sentence-transformers,['arxiv.org/abs/2108.08877']
ishan/bert-base-uncased-mnli,ishan,['arxiv.org/abs/1810.04805']
lapix/segformer-b3-finetuned-ccagt-400-300,lapix,"['doi.org/10.2139/ssrn.4126881)', 'bibtex']"
rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment,rohanrajpal,['bibtex']
segments-tobias/segformer-b0-finetuned-segments-sidewalk,segments-tobias,"['arxiv.org/abs/2105.15203', 'bibtex']"
lakahaga/novel_reading_tts,lakahaga,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
naver-clova-ix/donut-base-finetuned-zhtrainticket,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
tner/roberta-large-conll2003,tner,['bibtex']
facebook/wav2vec2-xls-r-300m-21-to-en,facebook,['arxiv.org/abs/2111.09296']
google/t5-efficient-xl,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-big-en-es,Helsinki-NLP,['bibtex']
flax-sentence-embeddings/all_datasets_v3_mpnet-base,flax-sentence-embeddings,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
j-hartmann/purchase-intention-english-roberta-large,j-hartmann,[{'title': 'The Power of Brand Selfies'}]
edumunozsala/beto_sentiment_analysis_es,edumunozsala,[{'title': 'Spanish Pre-Trained BERT Model and Evaluation Data'}]
deepmind/vision-perceiver-learned,deepmind,"['arxiv.org/abs/2107.14795', 'bibtex']"
MoritzLaurer/xtremedistil-l6-h256-mnli-fever-anli-ling-binary,MoritzLaurer,['arxiv.org/abs/2104.07179']
Narrativa/mT5-base-finetuned-tydiQA-xqa,Narrativa,['arxiv.org/abs/2010.11934']
climatebert/environmental-claims,climatebert,['arxiv.org/abs/2209.00507']
razent/SciFive-base-Pubmed,razent,['arxiv.org/abs/2106.03598']
it5/it5-large-news-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
deepmind/optical-flow-perceiver,deepmind,"['arxiv.org/abs/2107.14795', 'bibtex']"
facebook/xlm-roberta-xxl,facebook,"['arxiv.org/abs/2105.00572', 'bibtex']"
edbeeching/decision-transformer-gym-hopper-medium,edbeeching,['arxiv.org/abs/2106.01345']
facebook/detr-resnet-50-dc5-panoptic,facebook,"['arxiv.org/abs/2005.12872', 'bibtex']"
edbeeching/decision-transformer-gym-halfcheetah-expert,edbeeching,['arxiv.org/abs/2106.01345']
tuhailong/SimCSE-bert-base,tuhailong,['arxiv.org/abs/2104.08821']
yhavinga/t5-v1.1-base-dutch-cased,yhavinga,['arxiv.org/abs/2109.10686']
tner/roberta-large-bc5cdr,tner,['bibtex']
google/tapas-large-finetuned-tabfact,google,"['arxiv.org/abs/2010.00571', {'title': 'TabFact : A Large-scale Dataset for Table-based Fact Verification'}]"
wanyu/IteraTeR-ROBERTA-Intention-Classifier,wanyu,['arxiv.org/abs/2203.03802']
espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
nghuyong/ernie-3.0-medium-zh,nghuyong,"['arxiv.org/abs/2107.02137', {'title': 'Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation'}]"
khanhld/wav2vec2-base-vietnamese-160h,khanhld,['doi.org/10.5281/zenodo.6542357']
dbmdz/convbert-base-turkish-mc4-uncased,dbmdz,['doi.org/10.5281/zenodo.3770924}']
PrimeQA/tydiqa-primary-task-xlm-roberta-large,PrimeQA,"['arxiv.org/abs/2003.05002', 'bibtex']"
lrakotoson/scitldr-catts-xsum-ao,lrakotoson,[{'title': '{TLDR'}]
IIC/roberta-base-spanish-sqac,IIC,['arxiv.org/abs/2107.07253']
microsoft/swin-large-patch4-window7-224-in22k,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
GanjinZero/coder_eng_pp,GanjinZero,"['arxiv.org/abs/2204.00391', 'doi.org/10.48550/arxiv.2204.00391,']"
pucpr/clinicalnerpt-diagnostic,pucpr,['bibtex']
projecte-aina/roberta-base-ca-cased-qa,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
asafaya/bert-medium-arabic,asafaya,['bibtex']
it5/it5-base-news-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
xlm-clm-enfr-1024,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
allenai/aspire-contextualsentence-multim-compsci,allenai,['arxiv.org/abs/2111.08366']
josmunpen/mt5-small-spanish-summarization,josmunpen,['bibtex']
aubmindlab/araelectra-base-generator,aubmindlab,"['arxiv.org/abs/2012.15516', 'bibtex']"
AdapterHub/roberta-base-pf-snli,AdapterHub,['bibtex']
ganeshkharad/gk-hinglish-sentiment,ganeshkharad,['bibtex']
uer/chinese_roberta_L-6_H-768,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
facebook/deit-base-patch16-384,facebook,"['arxiv.org/abs/2012.12877', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
facebook/convnext-base-384-22k-1k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
sentence-transformers/roberta-large-nli-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
dangvantuan/sentence-camembert-base,dangvantuan,"['arxiv.org/abs/1908.10084', {'title': 'Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks'}]"
sijunhe/nezha-base-wwm,sijunhe,['arxiv.org/abs/1909.00204']
strombergnlp/dant5-large,strombergnlp,['arxiv.org/abs/2208.12097']
Visual-Attention-Network/van-small,Visual-Attention-Network,['arxiv.org/abs/2202.09741']
aware-ai/bart-squadv2,aware-ai,['arxiv.org/abs/1910.13461']
PlanTL-GOB-ES/roberta-large-bne-capitel-pos,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
facebook/xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022,facebook,"['arxiv.org/abs/2204.02967', 'doi.org/10.48550/arxiv.2204.02967,']"
nvidia/stt_es_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
sijunhe/nezha-large-wwm,sijunhe,['arxiv.org/abs/1909.00204']
facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur,facebook,['arxiv.org/abs/2204.02967']
gsarti/it5-small,gsarti,"['arxiv.org/abs/2203.03759', {'title': 'IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation'}]"
bigscience/T0p,bigscience,['arxiv.org/abs/2110.08207']
microsoft/tapex-large-finetuned-tabfact,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
clip-italian/clip-italian,clip-italian,['arxiv.org/abs/2103.00020']
google/t5-efficient-base-nl24,google,['arxiv.org/abs/2109.10686']
ozcangundes/mt5-small-turkish-summarization,ozcangundes,['arxiv.org/abs/2004.14900']
nvidia/stt_en_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
alibaba-pai/pai-bert-tiny-zh,alibaba-pai,"['arxiv.org/abs/2205.00258', {'title': 'EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing'}]"
moussaKam/barthez-sentiment-classification,moussaKam,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
openai/imagegpt-medium,openai,[{'title': 'Imagenet: A large-scale hierarchical image database'}]
ml6team/keyphrase-extraction-kbir-semeval2017,ml6team,['arxiv.org/abs/2112.08547']
csebuetnlp/mT5_m2o_arabic_crossSum,csebuetnlp,"['arxiv.org/abs/2112.08804', 'bibtex']"
SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net,SerdarHelli,[{'title': 'Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing'}]
pucpr/clinicalnerpt-medical,pucpr,['bibtex']
PrimeQA/tydiqa-boolean-answer-classifier,PrimeQA,"['arxiv.org/abs/2112.07772', 'doi.org/10.48550/arxiv.2206.08441,', {'title': 'Do Answers to Boolean Questions Need Explanations? Yes'}]"
microsoft/unispeech-sat-large,microsoft,['arxiv.org/abs/1912.07875']
flair/ner-multi-fast,flair,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
law-ai/InLegalBERT,law-ai,"['arxiv.org/abs/2209.06049', {'doi': '10.48550/ARXIV.2209.06049'}]"
pile-of-law/legalbert-large-1.7M-2,pile-of-law,['arxiv.org/abs/1907.11692']
jirmauritz/bert-multilingual-emoji,jirmauritz,"['arxiv.org/abs/1810.04805', 'bibtex']"
microsoft/resnet-101,microsoft,"['arxiv.org/abs/1512.03385', {'title': 'Deep residual learning for image recognition'}]"
GroNLP/gpt2-medium-dutch-embeddings,GroNLP,['arxiv.org/abs/2012.05628']
navteca/tapas-large-finetuned-wtq,navteca,['arxiv.org/abs/2004.02349']
megagonlabs/t5-base-japanese-web-8k,megagonlabs,['bibtex']
nghuyong/ernie-3.0-mini-zh,nghuyong,"['arxiv.org/abs/2107.02137', {'title': 'Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation'}]"
speechbrain/sepformer-wham16k-enhancement,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
microsoft/unispeech-sat-base-plus,microsoft,['arxiv.org/abs/1912.07875']
superb/hubert-base-superb-ks,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-multi,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
google/bert_uncased_L-4_H-768_A-12,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
facebook/convnext-xlarge-224-22k-1k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
studio-ousia/mluke-large,studio-ousia,['bibtex']
l3cube-pune/hing-bert-lid,l3cube-pune,['arxiv.org/abs/2204.08398']
HYPJUDY/layoutlmv3-base-finetuned-publaynet,HYPJUDY,[{'title': 'LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking'}]
facebook/data2vec-audio-base,facebook,['arxiv.org/abs/2202.03555']
allenai/tk-instruct-11b-def-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
facebook/data2vec-audio-large-960h,facebook,['arxiv.org/abs/2202.03555']
facebook/wav2vec2-base-10k-voxpopuli-ft-pl,facebook,['arxiv.org/abs/2101.00390']
NovelAI/genji-jp,NovelAI,['arxiv.org/abs/2104.09864']
NDugar/debertav3-mnli-snli-anli,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
deepmind/multimodal-perceiver,deepmind,"['arxiv.org/abs/2010.10864', 'bibtex']"
facebook/detr-resnet-101-dc5,facebook,"['arxiv.org/abs/2005.12872', 'bibtex']"
eugenesiow/msrn,eugenesiow,['arxiv.org/abs/2104.07566']
yangheng/deberta-v3-large-absa-v1.1,yangheng,"['arxiv.org/abs/2110.08604', 'bibtex']"
facebook/wav2vec2-xls-r-2b,facebook,['arxiv.org/abs/2111.09296']
KoboldAI/fairseq-dense-6.7B,KoboldAI,['arxiv.org/abs/2112.10684']
obrizum/all-mpnet-base-v2,obrizum,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
doc2query/msmarco-14langs-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
uer/chinese_roberta_L-12_H-128,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
facebook/wav2vec2-conformer-rope-large,facebook,['arxiv.org/abs/2010.05171']
lightonai/RITA_s,lightonai,"['arxiv.org/abs/2205.05789', {'title': 'RITA: a Study on Scaling Up Generative Protein Sequence Models'}]"
IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
nvidia/stt_en_citrinet_1024_gamma_0_25,nvidia,['arxiv.org/abs/2104.01721']
speechbrain/sepformer-whamr,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
Geotrend/distilbert-base-da-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
facebook/wav2vec2-base-10k-voxpopuli-ft-fr,facebook,['arxiv.org/abs/2101.00390']
PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
pysentimiento/robertuito-ner,pysentimiento,[{'title': 'LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation'}]
laituan245/molt5-large-smiles2caption,laituan245,['arxiv.org/abs/2204.11817']
microsoft/trocr-large-str,microsoft,['arxiv.org/abs/2109.10282']
Helsinki-NLP/opus-mt-tc-big-en-ko,Helsinki-NLP,['bibtex']
p208p2002/gpt2-squad-qg-hl,p208p2002,['arxiv.org/abs/1606.05250']
pyronear/rexnet1_0x,pyronear,"['arxiv.org/abs/2007.00992', 'bibtex']"
doc2query/msmarco-japanese-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
sonoisa/vl-t5-base-japanese,sonoisa,['arxiv.org/abs/2102.02779']
facebook/xm_transformer_600m-es_en-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
google/t5-efficient-base,google,['arxiv.org/abs/2109.10686']
UBC-NLP/AraT5-msa-small,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
unicamp-dl/ptt5-large-portuguese-vocab,unicamp-dl,[{'title': 'PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data'}]
pucpr/clinicalnerpt-healthcare,pucpr,['bibtex']
racai/distilbert-base-romanian-cased,racai,"['arxiv.org/abs/2112.12650', {'title': 'Distilling the Knowledge of Romanian BERTs Using Multiple Teachers'}]"
Voicemod/fastspeech2-en-male1,Voicemod,"['arxiv.org/abs/2006.04558', 'bibtex']"
microsoft/tapex-base-finetuned-wikisql,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
abhi1nandy2/EManuals_RoBERTa,abhi1nandy2,['bibtex']
readerbench/RoBERT-small,readerbench,[{'title': 'RoBERT--A Romanian BERT Model'}]
flax-community/t5-v1_1-base-wikisplit,flax-community,['arxiv.org/abs/1907.12461']
monsoon-nlp/bangla-electra,monsoon-nlp,['arxiv.org/abs/2004.07807']
hyunwoongko/reddit-3B,hyunwoongko,['arxiv.org/abs/1907.06616']
it5/it5-large-wiki-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
voidism/diffcse-roberta-base-trans,voidism,"['arxiv.org/abs/2204.10298', 'doi.org/10.48550/arXiv.2204.10298)', {'title': '{DiffCSE'}]"
DTAI-KULeuven/mbert-corona-tweets-belgium-topics,DTAI-KULeuven,['arxiv.org/abs/2104.09947']
Muennighoff/SGPT-1.3B-weightedmean-nli-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
thu-coai/CDial-GPT2_LCCC-base,thu-coai,['arxiv.org/abs/1901.08149']
Helsinki-NLP/opus-mt-tc-big-en-it,Helsinki-NLP,['bibtex']
sileod/roberta-base-discourse-marker-prediction,sileod,['bibtex']
facebook/s2t-small-covost2-de-en-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
climatebert/distilroberta-base-climate-d-s,climatebert,"['arxiv.org/abs/2110.12010', {'title': 'ClimateBERT: A Pretrained Language Model for Climate-Related Text'}]"
pucpr/clinicalnerpt-laboratory,pucpr,['bibtex']
optimum/bert-base-NER,optimum,"['arxiv.org/abs/1810.04805', 'bibtex']"
diversifix/diversiformer,diversifix,['arxiv.org/abs/2010.11934']
GroNLP/gpt2-small-dutch-embeddings,GroNLP,['arxiv.org/abs/2012.05628']
BSC-TeMU/roberta-base-bne,BSC-TeMU,['arxiv.org/abs/1907.11692']
edumunozsala/roberta_bne_sentiment_analysis_es,edumunozsala,"['arxiv.org/abs/2107.07253', 'bibtex']"
toloka/t5-large-for-text-aggregation,toloka,"['arxiv.org/abs/1910.10683', 'bibtex']"
l3cube-pune/MarathiSentiment,l3cube-pune,"['arxiv.org/abs/2103.11408', {'title': 'L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset'}]"
classla/roberta-base-frenk-hate,classla,"['arxiv.org/abs/1907.11692', 'bibtex']"
ktangri/gpt-neo-demo,ktangri,[{'title': 'The Pile: An 800GB Dataset of Diverse Text for Language Modeling'}]
ml6team/keyphrase-extraction-distilbert-openkp,ml6team,['arxiv.org/abs/1911.02671']
fxtentacle/wav2vec2-xls-r-1b-tevr,fxtentacle,"['arxiv.org/abs/2206.12693', 'doi.org/10.48550/arxiv.2206.12693,']"
facebook/data2vec-audio-base-10m,facebook,['arxiv.org/abs/2202.03555']
facebook/fastspeech2-en-200_speaker-cv4,facebook,"['arxiv.org/abs/2006.04558', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-en-ro,Helsinki-NLP,['bibtex']
csarron/roberta-base-squad-v1,csarron,['arxiv.org/abs/1907.11692']
microsoft/xclip-base-patch16-kinetics-600,microsoft,['arxiv.org/abs/2208.02816']
Milos/slovak-gpt-j-1.4B,Milos,['arxiv.org/abs/2104.09864']
sentence-transformers/nli-distilbert-base,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
xdai/mimic_roberta_base,xdai,['arxiv.org/abs/2204.06683']
w11wo/wav2vec2-xls-r-300m-zh-HK-lm-v2,w11wo,['arxiv.org/abs/2111.09296']
dbmdz/bert-tiny-historic-multilingual-cased,dbmdz,['arxiv.org/abs/1908.08962']
speechbrain/sepformer-wham,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
abhi1nandy2/Bible-roberta-base,abhi1nandy2,['bibtex']
voidism/diffcse-bert-base-uncased-trans,voidism,"['arxiv.org/abs/2204.10298', 'doi.org/10.48550/arXiv.2204.10298)', {'title': '{DiffCSE'}]"
facebook/maskformer-swin-large-ade,facebook,['arxiv.org/abs/2107.06278']
google/t5-efficient-small,google,['arxiv.org/abs/2109.10686']
ccdv/lsg-bart-base-4096,ccdv,"['arxiv.org/abs/1910.13461', 'bibtex']"
facebook/wmt21-dense-24-wide-x-en,facebook,"['arxiv.org/abs/2108.03265', {'title': 'Facebook AI’s WMT21 News Translation Task Submission'}]"
PlanTL-GOB-ES/roberta-base-bne-capitel-ner,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
pucpr/clinicalnerpt-sign,pucpr,['bibtex']
lgris/wav2vec2-large-xlsr-open-brazilian-portuguese,lgris,['arxiv.org/abs/2012.03411']
Helsinki-NLP/opus-mt-tc-big-ar-en,Helsinki-NLP,['bibtex']
ixa-ehu/roberta-eus-euscrawl-large-cased,ixa-ehu,['arxiv.org/abs/2203.08111']
speechbrain/google_speech_command_xvector,speechbrain,"['arxiv.org/abs/1804.03209', 'bibtex']"
NovelAI/genji-python-6B,NovelAI,['arxiv.org/abs/2104.09864']
pucpr/clinicalnerpt-disease,pucpr,['bibtex']
google/t5-efficient-small-dm256,google,['arxiv.org/abs/2109.10686']
andrejmiscic/simcls-scorer-billsum,andrejmiscic,"['arxiv.org/abs/2106.01890', 'bibtex']"
ml6team/keyphrase-generation-keybart-inspec,ml6team,['arxiv.org/abs/2112.08547']
Salesforce/mixqg-large,Salesforce,['arxiv.org/abs/2110.08175']
mrm8488/spanbert-finetuned-squadv1,mrm8488,['arxiv.org/abs/1907.10529']
Addedk/mbert-swedish-distilled-cased,Addedk,['arxiv.org/abs/2103.06418']
nickmuchi/yolos-small-finetuned-masks,nickmuchi,['arxiv.org/abs/2106.00666']
uer/albert-large-chinese-cluecorpussmall,uer,[{'title': 'Albert: A lite bert for self-supervised learning of language representations'}]
Helsinki-NLP/opus-mt-tc-big-en-fi,Helsinki-NLP,['bibtex']
facebook/tts_transformer-en-ljspeech,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
ml6team/keyphrase-extraction-kbir-kpcrowd,ml6team,['arxiv.org/abs/2112.08547']
google/multiberts-seed_0,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
w11wo/indonesian-roberta-base-sentiment-classifier,w11wo,['arxiv.org/abs/1907.11692']
uer/chinese_roberta_L-2_H-768,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
google/tapas-large-finetuned-sqa,google,['arxiv.org/abs/2004.02349']
MLRS/mBERTu,MLRS,['bibtex']
flax-community/indonesian-roberta-base,flax-community,['arxiv.org/abs/1907.11692']
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2,DataikuNLP,"['arxiv.org/abs/1908.10084', 'bibtex']"
airesearch/wangchanberta-base-wiki-newmm,airesearch,['arxiv.org/abs/1907.11692']
tner/roberta-large-mit-movie-trivia,tner,['bibtex']
dandelin/vilt-b32-finetuned-flickr30k,dandelin,['arxiv.org/abs/1505.04870']
mbartolo/roberta-large-synqa,mbartolo,['arxiv.org/abs/2002.00293']
facebook/s2t-small-mustc-en-it-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
sebastian-hofstaetter/prettr-distilbert-split_at_3-margin_mse-T2-msmarco,sebastian-hofstaetter,['arxiv.org/abs/2004.14255']
RVN/BERTovski,RVN,['bibtex']
BSC-TeMU/roberta-base-biomedical-es,BSC-TeMU,['arxiv.org/abs/2109.03570']
tner/roberta-large-wnut2017,tner,['bibtex']
facebook/convnext-large-384-22k-1k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
izumi-lab/bert-base-japanese-fin-additional,izumi-lab,"['arxiv.org/abs/1810.04805', {'title': '事前学習と追加事前学習による金融言語モデルの構築と検証'}]"
facebook/s2t-small-mustc-en-de-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
UBC-NLP/ptsm_t5_paraphraser,UBC-NLP,[{'title': 'Decay No More: A Persistent Twitter Dataset for Learning Social Meaning'}]
MoritzLaurer/DeBERTa-v3-small-mnli-fever-docnli-ling-2c,MoritzLaurer,['arxiv.org/abs/2104.07179']
patrickvonplaten/wav2vec2-base,patrickvonplaten,['arxiv.org/abs/2006.11477']
google/bert_uncased_L-8_H-128_A-2,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
it5/it5-large-question-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
rjbownes/Magic-The-Generating,rjbownes,[{'title': 'Fine Tuning GPT-2 for Magic the Gathering flavour text generation.'}]
mrm8488/mT5-small-finetuned-tydiqa-for-xqa,mrm8488,['arxiv.org/abs/2010.11934']
espnet/kan-bayashi_tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
GroNLP/gpt2-small-italian-embeddings,GroNLP,['arxiv.org/abs/2012.05628']
facebook/wav2vec2-xls-r-1b-21-to-en,facebook,['arxiv.org/abs/2111.09296']
PrimeQA/tydiqa-boolean-question-classifier,PrimeQA,"['arxiv.org/abs/1810.04805', 'doi.org/10.48550/arxiv.2206.08441,', 'bibtex']"
UBC-NLP/AraT5-tweet-base,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
benjamin/gpt2-large-wechsel-ukrainian,benjamin,['arxiv.org/abs/2112.06598']
bigscience/distill-bloom-1b3-10x,bigscience,['arxiv.org/abs/1909.08053']
facebook/tts_transformer-ru-cv7_css10,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
imdanboy/kss_tts_train_jets_raw_phn_korean_cleaner_korean_jaso_train.total_count.ave,imdanboy,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
doc2query/S2ORC-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
DTAI-KULeuven/robbertje-1-gb-bort,DTAI-KULeuven,['arxiv.org/abs/2101.05716']
fpianz/roberta-english-book-reviews-sentiment,fpianz,['arxiv.org/abs/1910.11769']
Milos/slovak-gpt-j-162M,Milos,['arxiv.org/abs/2104.09864']
megagonlabs/electra-base-japanese-discriminator,megagonlabs,['bibtex']
climatebert/distilroberta-base-climate-s,climatebert,"['arxiv.org/abs/2110.12010', {'title': 'ClimateBERT: A Pretrained Language Model for Climate-Related Text'}]"
iarfmoose/roberta-base-bulgarian-pos,iarfmoose,['arxiv.org/abs/1907.11692']
hfl/chinese-electra-base-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
google/byt5-xl,google,['arxiv.org/abs/1907.06292']
dumitrescustefan/bert-base-romanian-ner,dumitrescustefan,"['arxiv.org/abs/1909.01247', {'title': 'Introducing RONEC--the Romanian Named Entity Corpus'}]"
facebook/tts_transformer-fr-cv7_css10,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
kiheh85202/yolo,kiheh85202,"['arxiv.org/abs/2103.13413', 'bibtex']"
sentence-transformers/msmarco-MiniLM-L12-cos-v5,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
uer/chinese_roberta_L-2_H-256,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
superb/hubert-base-superb-er,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
uer/roberta-medium-word-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c,MoritzLaurer,['arxiv.org/abs/2104.07179']
albert-xlarge-v1,huggingface,"['arxiv.org/abs/1909.11942', 'bibtex']"
m3hrdadfi/bert-fa-base-uncased-farstail-mean-tokens,m3hrdadfi,[{'title': 'FarsTail: A Persian Natural Language Inference Dataset'}]
nytimesrd/paraphrase-MiniLM-L6-v2,nytimesrd,"['arxiv.org/abs/1908.10084', 'bibtex']"
microsoft/cvt-21-384-22k,microsoft,['arxiv.org/abs/2103.15808']
rinna/japanese-gpt-neox-small,rinna,['arxiv.org/abs/2101.00190']
laituan245/molt5-base,laituan245,['arxiv.org/abs/2204.11817']
google/t5-11b-ssm-tqa,google,['arxiv.org/abs/1910.10683']
uer/roberta-base-finetuned-ifeng-chinese,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
hyunwoongko/blenderbot-9B,hyunwoongko,['arxiv.org/abs/1907.06616']
pile-of-law/legalbert-large-1.7M-1,pile-of-law,['arxiv.org/abs/1907.11692']
doc2query/all-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
camembert/camembert-base-wikipedia-4gb,camembert,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
nvidia/stt_uk_citrinet_1024_gamma_0_25,nvidia,['arxiv.org/abs/2104.01721']
MCG-NJU/videomae-base-short,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
bayartsogt/albert-mongolian,bayartsogt,['arxiv.org/abs/1904.00962']
DTAI-KULeuven/robbertje-1-gb-shuffled,DTAI-KULeuven,['arxiv.org/abs/2101.05716']
Davlan/afro-xlmr-small,Davlan,['arxiv.org/abs/2204.06487']
marcosgg/bert-base-gl-cased,marcosgg,"['arxiv.org/abs/2106.13553', 'bibtex']"
ai4bharat/MultiIndicSentenceSummarizationSS,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
p208p2002/bart-squad-qg-hl,p208p2002,['arxiv.org/abs/1606.05250']
allenai/aspire-contextualsentence-multim-biomed,allenai,['arxiv.org/abs/2111.08366']
microsoft/amos,microsoft,[{'title': 'Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators'}]
RUCAIBox/mvp-story,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
allenai/aspire-biencoder-compsci-spec,allenai,['arxiv.org/abs/2111.08366']
m3hrdadfi/albert-fa-base-v2-sentiment-multi,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
csebuetnlp/mT5_m2o_russian_crossSum,csebuetnlp,"['arxiv.org/abs/2112.08804', 'bibtex']"
microsoft/trocr-base-str,microsoft,['arxiv.org/abs/2109.10282']
jcblaise/distilbert-tagalog-base-cased,jcblaise,[{'title': 'Establishing Baselines for Text Classification in Low-Resource Languages'}]
Geotrend/distilbert-base-de-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
unicamp-dl/translation-en-pt-t5,unicamp-dl,['bibtex']
xfbai/AMRBART-large,xfbai,['bibtex']
tner/deberta-v3-large-conll2003,tner,['bibtex']
uer/bart-chinese-4-768-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension'}]"
LeBenchmark/wav2vec2-FR-3K-base,LeBenchmark,[{'title': 'LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech'}]
tdopierre/ProtAugment-ParaphraseGenerator,tdopierre,"['arxiv.org/abs/2105.12995', {'title': 'ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning'}]"
rbawden/modern_french_normalisation,rbawden,"['doi.org/10.5281/zenodo.5865428', {'title': '{Automatic Normalisation of Early Modern French'}]"
google/roberta2roberta_L-24_wikisplit,google,['arxiv.org/abs/1907.12461']
it5/it5-base-headline-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
microsoft/unispeech-sat-base-plus-sd,microsoft,['arxiv.org/abs/1912.07875']
Milos/slovak-gpt-j-405M,Milos,['arxiv.org/abs/2104.09864']
razent/SciFive-base-PMC,razent,['arxiv.org/abs/2106.03598']
imdanboy/jets,imdanboy,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MCG-NJU/videomae-base-finetuned-ssv2,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
espnet/kan-bayashi_jsut_vits_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
lighthouse/mdeberta-v3-base-kor-further,lighthouse,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
clampert/multilingual-sentiment-covid19,clampert,[{'title': 'Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis'}]
IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
michiyasunaga/LinkBERT-large,michiyasunaga,['arxiv.org/abs/2203.15827']
projecte-aina/roberta-base-ca-v2-cased-pos,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
microsoft/conditional-detr-resnet-50,microsoft,"['arxiv.org/abs/2108.06152', 'bibtex']"
biu-nlp/cdlm,biu-nlp,[{'title': 'Cross-Document Language Modeling'}]
mrm8488/deberta-v3-small-finetuned-mnli,mrm8488,['arxiv.org/abs/2006.03654']
unicamp-dl/ptt5-small-portuguese-vocab,unicamp-dl,[{'title': 'PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data'}]
climatebert/distilroberta-base-climate-d,climatebert,"['arxiv.org/abs/2110.12010', {'title': 'ClimateBERT: A Pretrained Language Model for Climate-Related Text'}]"
BSC-TeMU/roberta-large-bne-sqac,BSC-TeMU,['arxiv.org/abs/1907.11692']
pucpr/clinicalnerpt-therapeutic,pucpr,['bibtex']
CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
sentence-transformers/gtr-t5-xxl,sentence-transformers,['arxiv.org/abs/2112.07899']
espnet/kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
uer/roberta-base-wwm-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]"
microsoft/trocr-large-stage1,microsoft,['arxiv.org/abs/2109.10282']
Helsinki-NLP/opus-mt-tc-big-he-en,Helsinki-NLP,['bibtex']
joaoalvarenga/bloom-8bit,joaoalvarenga,['arxiv.org/abs/2106.09685']
eugenesiow/edsr,eugenesiow,['arxiv.org/abs/1707.02921']
julian-schelb/roberta-ner-multilingual,julian-schelb,['arxiv.org/abs/1911.02116']
Voicemod/fastspeech2-en-ljspeech,Voicemod,"['arxiv.org/abs/2006.04558', 'bibtex']"
patrickvonplaten/bart-large-fp32,patrickvonplaten,"['arxiv.org/abs/1910.13461', 'bibtex']"
abhi1nandy2/Craft-bionlp-roberta-base,abhi1nandy2,['bibtex']
pucpr/clinicalnerpt-finding,pucpr,['bibtex']
speechbrain/sepformer-wham-enhancement,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
rufimelo/Legal-BERTimbau-sts-base,rufimelo,['bibtex']
qarib/bert-base-qarib_far,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
facebook/wav2vec2-xls-r-300m-en-to-15,facebook,['arxiv.org/abs/2111.09296']
microsoft/tapex-large-sql-execution,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
mrm8488/mobilebert-uncased-finetuned-squadv2,mrm8488,['arxiv.org/abs/2004.02984']
lordtt13/COVID-SciBERT,lordtt13,['arxiv.org/abs/1903.10676']
google/tapas-medium-finetuned-wikisql-supervised,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
indobenchmark/indobert-lite-large-p1,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
google/long-t5-local-large,google,[{'title': 'LongT5: Efficient Text-To-Text Transformer for Long Sequences'}]
speechbrain/sepformer-whamr16k,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
google/tapas-large-finetuned-wikisql-supervised,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
microsoft/unispeech-sat-large-sv,microsoft,['arxiv.org/abs/1912.07875']
Visual-Attention-Network/van-tiny,Visual-Attention-Network,['arxiv.org/abs/2202.09741']
sgugger/resnet50d,sgugger,"['arxiv.org/abs/1512.03385', 'bibtex']"
microsoft/cvt-21,microsoft,['arxiv.org/abs/2103.15808']
BSC-TeMU/roberta-base-bne-sqac,BSC-TeMU,['arxiv.org/abs/1907.11692']
tscholak/2jrayxos,tscholak,"['arxiv.org/abs/2109.05093', 'bibtex']"
questgen/all-mpnet-base-v2-feature-extraction-pipeline,questgen,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
fav-kky/wav2vec2-base-cs-80k-ClTRUS,fav-kky,"['arxiv.org/abs/2206.07627', {'title': 'Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of {C'}]"
uer/bart-chinese-6-960-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension'}]"
google/tapas-medium-finetuned-wtq,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-en-tr,Helsinki-NLP,['bibtex']
m3hrdadfi/albert-fa-base-v2-clf-persiannews,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
Hate-speech-CNERG/english-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
microsoft/xclip-large-patch14-kinetics-600,microsoft,['arxiv.org/abs/2208.02816']
google/bert_uncased_L-2_H-512_A-8,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
tau/bart-base-sled,tau,"['arxiv.org/abs/2208.00748', {'title': 'Efficient Long-Text Understanding with Short-Text Models'}]"
QCRI/bert-base-multilingual-cased-pos-english,QCRI,[{'title': 'Analyzing Encoded Concepts in Transformer Language Models'}]
google/t5-xl-ssm-nq,google,['arxiv.org/abs/1910.10683']
Helsinki-NLP/opus-mt-tc-big-zh-ja,Helsinki-NLP,['bibtex']
sentence-transformers/xlm-r-large-en-ko-nli-ststb,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-kinyarwanda,mbeukman,['arxiv.org/abs/2103.11811']
indobenchmark/indobert-lite-large-p2,indobenchmark,"['arxiv.org/abs/2009.05387', {'title': 'IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding'}]"
amoux/roberta-cord19-1M7k,amoux,[{'title': 'CORD-19: The Covid-19 Open Research Dataset'}]
izumi-lab/electra-base-japanese-discriminator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '事前学習と追加事前学習による金融言語モデルの構築と検証'}]"
aubmindlab/aragpt2-mega-detector-long,aubmindlab,['arxiv.org/abs/2012.15520']
Helsinki-NLP/opus-mt-tc-big-it-en,Helsinki-NLP,['bibtex']
tilomichel/mT5-base-GermanQuAD-e2e-qg,tilomichel,['arxiv.org/abs/2010.11934']
gokceuludogan/ChemBERTaLM,gokceuludogan,"['doi.org/10.1093/bioinformatics/btac482}', 'bibtex']"
doc2query/msmarco-hindi-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
eleldar/theme-classification,eleldar,['arxiv.org/abs/1910.13461']
megagonlabs/transformers-ud-japanese-electra-base-ginza,megagonlabs,['bibtex']
google/bert2bert_L-24_wmt_en_de,google,['arxiv.org/abs/1907.12461']
lysandre/tapas-temporary-repo,lysandre,['arxiv.org/abs/2004.02349']
microsoft/ssr-base,microsoft,['arxiv.org/abs/2101.00416']
BSC-TeMU/roberta-large-bne,BSC-TeMU,['arxiv.org/abs/1907.11692']
Hate-speech-CNERG/dehatebert-mono-german,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
facebook/wav2vec2-base-es-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
superb/wav2vec2-base-superb-ic,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
abhitopia/question-answer-generation,abhitopia,['arxiv.org/abs/1910.10683']
biu-nlp/superpal,biu-nlp,['bibtex']
facebook/detr-resnet-50-dc5,facebook,"['arxiv.org/abs/2005.12872', 'bibtex']"
Voicelab/sbert-base-cased-pl,Voicelab,['arxiv.org/abs/1908.10084']
csebuetnlp/banglat5_nmt_bn_en,csebuetnlp,"['arxiv.org/abs/2205.11081', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
thu-coai/EVA2.0-base,thu-coai,"['arxiv.org/abs/2108.01547', {'title': 'EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training'}]"
Graphcore/gptj-mnli,Graphcore,['arxiv.org/abs/2104.09864']
superb/hubert-base-superb-ic,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
optimum/vit-base-patch16-224,optimum,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
espnet/english_male_ryanspeech_tacotron,espnet,[{'title': 'RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis'}]
sarnikowski/electra-small-generator-da-256-cased,sarnikowski,['arxiv.org/abs/2003.10555']
TalTechNLP/voxlingua107-epaca-tdnn-ce,TalTechNLP,[{'title': '{VoxLingua107'}]
facebook/wav2vec2-xls-r-2b-21-to-en,facebook,['arxiv.org/abs/2111.09296']
facebook/s2t-small-mustc-en-ru-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
superb/hubert-base-superb-sid,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
camembert/camembert-base-oscar-4gb,camembert,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
Danish-summarisation/dansum-mt5-base-v1,Danish-summarisation,['arxiv.org/abs/1804.11283']
svalabs/mt5-large-german-query-gen-v1,svalabs,['arxiv.org/abs/1904.08375']
csebuetnlp/banglat5_nmt_en_bn,csebuetnlp,"['arxiv.org/abs/2205.11081', 'bibtex']"
ml6team/keyphrase-generation-t5-small-openkp,ml6team,['arxiv.org/abs/1911.02671']
apple/deeplabv3-mobilevit-x-small,apple,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
benjamin/roberta-base-wechsel-french,benjamin,['bibtex']
google/t5-11b-ssm-nq,google,['arxiv.org/abs/1910.10683']
microsoft/cvt-13-384-22k,microsoft,['arxiv.org/abs/2103.15808']
ulysses-camara/legal-bert-pt-br,ulysses-camara,['bibtex']
Helsinki-NLP/opus-mt-tc-big-zle-en,Helsinki-NLP,['bibtex']
HooshvareLab/bert-fa-base-uncased-sentiment-digikala,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
pszemraj/grammar-synthesis-small,pszemraj,['arxiv.org/abs/2107.06751']
Luyu/bert-base-mdoc-bm25,Luyu,[{'title': 'Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline'}]
edbeeching/decision-transformer-gym-walker2d-expert,edbeeching,['arxiv.org/abs/2106.01345']
flax-sentence-embeddings/all_datasets_v3_distilroberta-base,flax-sentence-embeddings,"['arxiv.org/abs/1810.09305', 'doi.org/10.18653/v1/d15-1075),']"
flair/frame-english,flair,[{'title': 'FLAIR: An easy-to-use framework for state-of-the-art NLP'}]
byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp,byan,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
hfl/chinese-legal-electra-large-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
pysentimiento/robertuito-base-uncased,pysentimiento,['arxiv.org/abs/2111.09453']
illuin/camembert-base-fquad,illuin,[{'title': 'FQuAD: French Question Answering Dataset'}]
julien-c/DPRNNTasNet-ks16_WHAM_sepclean,julien-c,[{'title': 'Asteroid: the {PyTorch'}]
google/multiberts-seed_0-step_2000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
ml6team/keyphrase-extraction-kbir-kptimes,ml6team,['arxiv.org/abs/2112.08547']
PrimeQA/squad-v1-roberta-large,PrimeQA,"['arxiv.org/abs/1907.11692', 'bibtex']"
McGill-NLP/roberta-large-faithcritic,McGill-NLP,"['arxiv.org/abs/2204.10757', {'title': 'FaithDial: A Faithful Benchmark for Information-Seeking Dialogue'}]"
PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer,PlanTL-GOB-ES,['arxiv.org/abs/1907.11692']
sagteam/xlm-roberta-large-sag,sagteam,"['arxiv.org/abs/1911.02116', {'title': 'An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets'}]"
facebook/wav2vec2-xls-r-1b-en-to-15,facebook,['arxiv.org/abs/2111.09296']
doc2query/msmarco-t5-small-v1,doc2query,['arxiv.org/abs/1904.08375']
ibm/roberta-large-vira-intents,ibm,['arxiv.org/abs/2205.11966']
frgfm/rexnet1_0x,frgfm,"['arxiv.org/abs/2007.00992', 'bibtex']"
facebook/wav2vec2-base-10k-voxpopuli-ft-en,facebook,['arxiv.org/abs/2101.00390']
Helsinki-NLP/opus-mt-tc-big-cat_oci_spa-en,Helsinki-NLP,['bibtex']
tner/bertweet-large-wnut2017,tner,['bibtex']
projecte-aina/roberta-base-ca-v2-cased-te,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
facebook/genre-kilt,facebook,"['arxiv.org/abs/2010.00904', {'title': 'Autoregressive Entity Retrieval'}]"
frgfm/rexnet1_5x,frgfm,"['arxiv.org/abs/2007.00992', 'bibtex']"
m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-binary,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
AdapterHub/bert-base-uncased-pf-emotion,AdapterHub,['bibtex']
xlm-roberta-large-finetuned-conll02-dutch,huggingface,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
IDEA-CCNL/Taiyi-CLIP-RoBERTa-326M-ViT-H-Chinese,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
google/t5-efficient-large,google,['arxiv.org/abs/2109.10686']
l3cube-pune/hindi-marathi-dev-bert-scratch,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-msa-half,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
Intel/bert-base-uncased-sparse-85-unstructured-pruneofa,Intel,['arxiv.org/abs/2111.05754']
BartekK/distilHerBERT-base-cased,BartekK,['arxiv.org/abs/1910.01108']
alibaba-pai/pai-dkplm-financial-base-zh,alibaba-pai,"['arxiv.org/abs/2205.00258', {'title': 'EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing'}]"
uhhlt/bert-based-uncased-hatespeech-movies,uhhlt,[{'title': 'How Hateful are Movies? A Study and Prediction on Movie Subtitles'}]
w11wo/sundanese-roberta-base,w11wo,"['arxiv.org/abs/1907.11692', 'doi.org/10.21203/rs.3.rs-907893/v1}', 'bibtex']"
w11wo/indonesian-roberta-base-posp-tagger,w11wo,['arxiv.org/abs/1907.11692']
facebook/wav2vec2-large-slavic-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
l3cube-pune/hindi-bert-v2,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
gchhablani/fnet-base-finetuned-sst2,gchhablani,['arxiv.org/abs/2105.03824']
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
facebook/levit-256,facebook,['arxiv.org/abs/2104.01136']
GanjinZero/coder_eng,GanjinZero,"['doi.org/10.1016/j.jbi.2021.103983},', {'title': 'CODER: Knowledge-infused cross-lingual medical term embedding for term normalization'}]"
lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet,lichenda,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
HooshvareLab/bert-fa-base-uncased-ner-arman,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
Intel/bert-large-uncased-sparse-90-unstructured-pruneofa,Intel,['arxiv.org/abs/2111.05754']
xlm-roberta-large-finetuned-conll02-spanish,huggingface,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
agne/jobGBERT,agne,"['doi.org/10.5281/zenodo.6497853)', 'bibtex']"
tner/roberta-large-fin,tner,['bibtex']
facebook/maskformer-swin-large-coco,facebook,['arxiv.org/abs/2107.06278']
north/t5_base_NCC,north,['arxiv.org/abs/2104.09617']
MCG-NJU/videomae-large-finetuned-kinetics,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
facebook/convnext-base-224-22k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
aiknowyou/mt5-base-it-paraphraser,aiknowyou,['arxiv.org/abs/2010.11934']
google/tapas-small-finetuned-sqa,google,['arxiv.org/abs/2004.02349']
ptaszynski/yacis-electra-small-japanese,ptaszynski,[{'title': '日本語大規模ブログコーパスYACISに基づいたELECTRA事前学習済み言語モデルの作成及び性能評価'}]
sentence-transformers/nli-distilbert-base-max-pooling,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
ncoop57/DiGPTame-medium,ncoop57,['arxiv.org/abs/1911.00536']
allenai/led-base-16384-cochrane,allenai,['arxiv.org/abs/2104.06486']
agne/jobBERT-de,agne,"['doi.org/10.5281/zenodo.6497853)', 'bibtex']"
AriakimTaiyo/gpt2-chat,AriakimTaiyo,"['arxiv.org/abs/1910.09700', {'title': 'Language models are unsupervised multitask learners'}]"
khalidalt/DeBERTa-v3-large-mnli,khalidalt,['arxiv.org/abs/2006.03654']
flax-sentence-embeddings/all_datasets_v4_mpnet-base,flax-sentence-embeddings,"['arxiv.org/abs/1810.09305', 'doi.org/10.18653/v1/d15-1075),']"
Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
asafaya/hubert-large-arabic-ft,asafaya,['arxiv.org/abs/2106.07447']
uer/roberta-tiny-word-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
neuropark/sahajBERT-NER,neuropark,['bibtex']
flax-community/byt5-base-wikisplit,flax-community,['arxiv.org/abs/1907.12461']
tner/roberta-large-tweebank-ner,tner,['bibtex']
apple/mobilevit-x-small,apple,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
tner/roberta-large-btc,tner,['bibtex']
IDEA-CCNL/Randeng-BART-759M-Chinese-BertTokenizer,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
mrm8488/deberta-v3-small-finetuned-cola,mrm8488,['arxiv.org/abs/2006.03654']
google/byt5-xxl,google,['arxiv.org/abs/1907.06292']
facebook/genre-linking-blink,facebook,"['arxiv.org/abs/2010.00904', {'title': 'Autoregressive Entity Retrieval'}]"
Helsinki-NLP/opus-mt-tc-big-ko-en,Helsinki-NLP,['bibtex']
hustvl/yolos-small-dwr,hustvl,"['arxiv.org/abs/2106.00666', 'bibtex']"
Geotrend/bert-base-hi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
ixa-ehu/roberta-eus-mc4-base-cased,ixa-ehu,['arxiv.org/abs/2203.08111']
hustvl/yolos-small-300,hustvl,"['arxiv.org/abs/2106.00666', 'bibtex']"
kornosk/bert-election2020-twitter-stance-biden-KE-MLM,kornosk,[{'title': 'Knowledge Enhanced Masked Language Model for Stance Detection'}]
lincoln/flaubert-mlsum-topic-classification,lincoln,[{'title': 'MLSUM: The Multilingual Summarization Corpus'}]
facebook/convnext-large-224,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
Cedille/fr-boris,Cedille,['arxiv.org/abs/2202.03371']
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer6_n_fft512_hop_length2-truncated-a63357,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ml6team/keyphrase-extraction-distilbert-kptimes,ml6team,['arxiv.org/abs/1911.12559']
google/bert_uncased_L-4_H-128_A-2,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
IDEA-CCNL/Erlangshen-MegatronBert-3.9B-Chinese,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
TweebankNLP/bertweet-tb2_ewt-pos-tagging,TweebankNLP,[{'title': 'Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis'}]
google/t5-large-ssm,google,['arxiv.org/abs/1910.10683']
google/multiberts-seed_4-step_2000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
imohammad12/GRS-Grammar-Checker-DeBerta,imohammad12,['bibtex']
rsuwaileh/IDRISI-LMR-EN-timebased-typeless,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
Geotrend/bert-base-ru-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco,sebastian-hofstaetter,['arxiv.org/abs/2010.02666']
doc2query/msmarco-chinese-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
ixa-ehu/roberta-eus-cc100-base-cased,ixa-ehu,['arxiv.org/abs/2203.08111']
microsoft/beit-large-patch16-224,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
fav-kky/FERNET-News,fav-kky,['arxiv.org/abs/2107.10042']
tner/roberta-large-mit-restaurant,tner,['bibtex']
praf-choub/bart-CaPE-cnn,praf-choub,"['arxiv.org/abs/2110.07166', 'doi.org/10.48550/arxiv.2110.07166,']"
EMBO/sd-ner-v2,EMBO,['doi.org/10.1038/nmeth.4471).']
tner/roberta-large-tweetner7-selflabel2021,tner,['bibtex']
XLab/rst-word-sense-disambiguation-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
microsoft/dit-large-finetuned-rvlcdip,microsoft,"['arxiv.org/abs/2203.02378', {'title': 'Building a test collection for complex document information processing'}]"
mbartolo/electra-large-synqa,mbartolo,['arxiv.org/abs/2002.00293']
tner/roberta-large-bionlp2004,tner,['bibtex']
facebook/regnet-y-320-seer-in1k,facebook,['arxiv.org/abs/2202.08360']
sharif-dal/dal-bert,sharif-dal,['arxiv.org/abs/1810.04805']
m3hrdadfi/albert-fa-base-v2,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
projecte-aina/roberta-base-ca-v2-cased-qa,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
tomh/toxigen_hatebert,tomh,"['arxiv.org/abs/2203.09509', 'bibtex']"
Hate-speech-CNERG/dehatebert-mono-polish,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
SenseTime/deformable-detr-single-scale,SenseTime,"['arxiv.org/abs/2010.04159', 'doi.org/10.48550/arxiv.2010.04159,']"
facebook/vit-mae-huge,facebook,"['arxiv.org/abs/2111.06377', 'bibtex']"
mrm8488/bertin-gpt-j-6B-ES-8bit,mrm8488,['arxiv.org/abs/2106.09685']
sijunhe/nezha-cn-large,sijunhe,['arxiv.org/abs/1909.00204']
facebook/m2m100-12B-last-ckpt,facebook,['arxiv.org/abs/2010.11125']
dragonSwing/viwav2vec2-base-3k,dragonSwing,['arxiv.org/abs/2006.11477']
anhdungitvn/vi-xlm-roberta-base,anhdungitvn,[{'title': 'x'}]
Lurunchik/nf-cats,Lurunchik,"['doi.org/10.1145/3477495.3531926},']"
facebook/maskformer-swin-small-ade,facebook,['arxiv.org/abs/2107.06278']
ncfrey/ChemGPT-19M,ncfrey,['bibtex']
espnet/kan-bayashi_ljspeech_tts_train_joint_conformer_fastspeech2_hifigan_raw-truncated-af8fe0,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Mathking/pubmedbert-abs_pri-sec_out,Mathking,['bibtex']
arm-on/BERTweet-FA,arm-on,['arxiv.org/abs/1810.04805']
Kowsher/bangla-bert,Kowsher,['arxiv.org/abs/1810.04805']
castorini/afriberta_base,castorini,['bibtex']
Geotrend/distilbert-base-fr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
HUPD/hupd-distilroberta-base,HUPD,"[{'title': 'The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications'}]"
l3cube-pune/hing-roberta-mixed,l3cube-pune,['arxiv.org/abs/2204.08398']
hfl/chinese-legal-electra-base-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
CogComp/bart-faithful-summary-detector,CogComp,['bibtex']
ai4bharat/IndicBART-XLSum,ai4bharat,['arxiv.org/abs/2109.02903']
nvidia/stt_de_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
slone/mbart-large-51-mul-myv-v1,slone,['arxiv.org/abs/2209.09368']
bigscience/sgpt-bloom-7b1-msmarco,bigscience,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
nlpaueb/sec-bert-num,nlpaueb,"['arxiv.org/abs/2203.06482', {'title': 'FiNER: Financial Numeric Entity Recognition for XBRL Tagging'}]"
Finnish-NLP/roberta-large-finnish,Finnish-NLP,['arxiv.org/abs/1907.11692']
facebook/tts_transformer-ar-cv7,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
google/t5-efficient-large-nl32,google,['arxiv.org/abs/2109.10686']
csarron/mobilebert-uncased-squad-v2,csarron,['arxiv.org/abs/2004.02984']
imdanboy/kss_tts_train_jets_raw_phn_null_g2pk_train.total_count.ave,imdanboy,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
IIC/wav2vec2-spanish-multilibrispeech,IIC,['arxiv.org/abs/2006.13979']
microsoft/beit-large-patch16-224-pt22k,microsoft,"['arxiv.org/abs/2106.08254', 'bibtex']"
DanL/scientific-challenges-and-directions,DanL,['arxiv.org/abs/2108.13751']
Tristan/opt-66b-copy,Tristan,['arxiv.org/abs/2205.01068']
aapot/wav2vec2-xlsr-1b-finnish-lm-v2,aapot,['arxiv.org/abs/2111.09296']
tner/twitter-roberta-base-dec2021-tweetner7-continuous,tner,['bibtex']
allenai/tk-instruct-3b-def-pos-neg,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
nlpaueb/bert-base-uncased-echr,nlpaueb,['bibtex']
tner/roberta-large-tweetner7-selflabel2020,tner,['bibtex']
google/t5-efficient-small-el16,google,['arxiv.org/abs/2109.10686']
CuongLD/wav2vec2-large-xlsr-vietnamese,CuongLD,['arxiv.org/abs/2006.11477']
SenseTime/deformable-detr-with-box-refine-two-stage,SenseTime,"['arxiv.org/abs/2010.04159', 'doi.org/10.48550/arxiv.2010.04159,']"
tugstugi/bert-base-mongolian-cased,tugstugi,['arxiv.org/abs/1810.04805']
Geotrend/bert-base-10lang-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Multilingual BERT'}]
malteos/gpt2-xl-wechsel-german,malteos,['arxiv.org/abs/2112.06598']
google/ncsnpp-ffhq-256,google,['arxiv.org/abs/2011.13456']
inokufu/flaubert-base-uncased-xnli-sts-finetuned-education,inokufu,['arxiv.org/abs/1810.04805']
it5/it5-large-question-answering,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
tals/albert-base-mnli,tals,['bibtex']
hfl/chinese-legal-electra-large-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
facebook/vit-msn-small,facebook,"['arxiv.org/abs/2204.07141', {'title': 'Masked Siamese Networks for Label-Efficient Learning'}]"
facebook/tts_transformer-tr-cv7,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
microsoft/unispeech-1350-en-353-fr-ft-1h,microsoft,['arxiv.org/abs/2101.07597']
nvidia/stt_es_conformer_ctc_large,nvidia,['arxiv.org/abs/2005.08100']
uer/roberta-tiny-wwm-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]"
Geotrend/distilbert-base-en-fr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
eugenesiow/drln-bam,eugenesiow,['arxiv.org/abs/1906.12021']
tner/roberta-large-ttc,tner,['bibtex']
DeepPavlov/roberta-large-winogrande,DeepPavlov,"['arxiv.org/abs/1907.11692', {'title': 'WinoGrande: An Adversarial Winograd Schema Challenge at Scale'}]"
HiTZ/A2T_RoBERTa_SMFA_ACE-arg,HiTZ,"['arxiv.org/abs/2104.14690', 'bibtex']"
ptaszynski/japan-orientalization-pl,ptaszynski,[{'title': 'Finetuned HerBERT model for detecting orientalization of Japan in newspaper articles'}]
XLab/rst-all-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
naver-clova-ix/donut-proto,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-tr-en,Helsinki-NLP,['bibtex']
airesearch/wangchanberta-base-wiki-spm,airesearch,['arxiv.org/abs/1907.11692']
nvidia/tts_en_fastpitch,nvidia,['arxiv.org/abs/2006.06873']
facebook/maskformer-swin-tiny-coco,facebook,['arxiv.org/abs/2107.06278']
it5/it5-small-wiki-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
allenai/longformer-scico,allenai,[{'title': 'SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts'}]
globuslabs/ScholarBERT,globuslabs,['arxiv.org/abs/2205.11342']
PrimeQA/t5-base-table-question-generator,PrimeQA,[{'title': 'Topic Transferable Table Question Answering'}]
it5/it5-large-headline-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
sberbank-ai/bert-base-NER-reptile-5-datasets,sberbank-ai,['arxiv.org/abs/2010.02405']
dbmdz/bert-base-historic-multilingual-cased,dbmdz,['arxiv.org/abs/2205.15575']
hfl/chinese-electra-small-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
MEDT/Chatbot_Medium,MEDT,['arxiv.org/abs/1911.00536']
Voicelab/sbert-large-cased-pl,Voicelab,['arxiv.org/abs/1908.10084']
facebook/convnext-base-224-22k-1k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
Geotrend/bert-base-es-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
mindee/fasterrcnn_mobilenet_v3_large_fpn,mindee,"['arxiv.org/abs/1506.01497', 'bibtex']"
uer/chinese_roberta_L-4_H-128,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
google/t5-efficient-large-nl36,google,['arxiv.org/abs/2109.10686']
naver-clova-ix/donut-base-finetuned-cord-v1-2560,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
sentence-transformers/msmarco-bert-co-condensor,sentence-transformers,['arxiv.org/abs/2108.05540']
julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train,julien-c,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Neo87z1/STEKGramarChecker,Neo87z1,['arxiv.org/abs/1702.04066']
sentence-transformers/distilroberta-base-msmarco-v1,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
HooshvareLab/bert-fa-base-uncased-ner-peyma,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
unicamp-dl/mt5-base-mmarco-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
owaiskha9654/Multi-Label-Classification-of-PubMed-Articles,owaiskha9654,['arxiv.org/abs/1706.03762']
nvidia/stt_zh_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
thu-coai/CDial-GPT_LCCC-base,thu-coai,['arxiv.org/abs/1901.08149']
sail/poolformer_m48,sail,"['arxiv.org/abs/2111.11418', {'title': 'MetaFormer is Actually What You Need for Vision'}]"
north/t5_small_NCC_lm,north,['arxiv.org/abs/2104.09617']
google/tapas-small-finetuned-wikisql-supervised,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
ptaszynski/yacis-electra-small-japanese-cyberbullying,ptaszynski,[{'title': '日本語大規模ブログコーパスYACISに基づいたELECTRA事前学習済み言語モデルの作成及び性能評価'}]
eugenesiow/han,eugenesiow,['arxiv.org/abs/2008.08767']
tau/bart-base-sled-govreport,tau,"['arxiv.org/abs/2104.02112', {'title': 'Efficient Long-Text Understanding with Short-Text Models'}]"
gchhablani/bert-base-cased-finetuned-qnli,gchhablani,['arxiv.org/abs/2105.03824']
munggok/Roberta-Large-Indo,munggok,['arxiv.org/abs/1907.11692']
wyu1/DictBERT,wyu1,[{'title': 'Dict-BERT: Enhancing Language Model Pre-training with Dictionary'}]
NbAiLab/nb-gpt-j-6B,NbAiLab,"['arxiv.org/abs/2104.09864', {'title': 'Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model'}]"
qarib/bert-base-qarib_far_9920k,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
KBLab/bart-base-swedish-cased,KBLab,['arxiv.org/abs/1910.13461']
uer/chinese_roberta_L-4_H-768,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
andrejmiscic/simcls-scorer-cnndm,andrejmiscic,"['arxiv.org/abs/2106.01890', 'bibtex']"
ignatius/cyT5-small,ignatius,"['arxiv.org/abs/2205.02545', {'title': 'Introducing the Welsh text summarisation dataset and baseline systems'}]"
HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg_ACE-arg,HiTZ,"['arxiv.org/abs/2104.14690', 'bibtex']"
Matthijs/deeplabv3-mobilevit-small,Matthijs,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
uer/roberta-small-wwm-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]"
hfl/chinese-electra-180g-base-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
Geotrend/bert-base-en-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
pszemraj/t5-large-for-lexical-analysis,pszemraj,['arxiv.org/abs/2105.08209']
flax-community/pino-bigbird-roberta-base,flax-community,['arxiv.org/abs/2007.14062']
google/ncsnpp-ffhq-1024,google,['arxiv.org/abs/2011.13456']
phjhk/hklegal-xlm-r-base,phjhk,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
tae898/emoberta-large,tae898,['arxiv.org/abs/2108.12009']
tner/roberta-large-tweetner7-continuous,tner,['bibtex']
hfl/chinese-electra-180g-small-ex-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
flax-sentence-embeddings/mpnet_stackexchange_v1,flax-sentence-embeddings,['doi.org/10.1145/3404835.3462804)']
Narrativa/byt5-base-finetuned-tweet-qa,Narrativa,['arxiv.org/abs/1907.06292']
DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support,DTAI-KULeuven,['arxiv.org/abs/2104.09947']
gmurro/bart-large-finetuned-filtered-spotify-podcast-summ,gmurro,['arxiv.org/abs/2004.04270']
facebook/convnext-base-384,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
facebook/wav2vec2-large-it-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
facebook/s2t-wav2vec2-large-en-tr,facebook,"['arxiv.org/abs/2104.06678', 'bibtex']"
junnyu/structbert-large-zh,junnyu,"['arxiv.org/abs/1908.04577', {'title': 'Structbert: Incorporating language structures into pre-training for deep language understanding'}]"
XLab/rst-topic-classification-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
microsoft/wavlm-base-sv,microsoft,['arxiv.org/abs/2110.13900']
sentence-transformers/nli-bert-large-max-pooling,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
openai/imagegpt-large,openai,[{'title': 'Imagenet: A large-scale hierarchical image database'}]
naver-clova-ix/donut-base-finetuned-cord-v1,naver-clova-ix,"['arxiv.org/abs/2111.15664', 'bibtex']"
tner/roberta-large-tweetner7-all,tner,['bibtex']
allenai/aspire-sentence-embedder,allenai,['arxiv.org/abs/2111.08366']
flax-community/dalle-mini,flax-community,['arxiv.org/abs/1910.13461']
Luyu/bert-base-mdoc-hdct,Luyu,[{'title': 'Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline'}]
tner/roberta-large-tweetner7-selflabel2021-continuous,tner,['bibtex']
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
XLab/rst-temporal-reasoning-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
google/t5-efficient-tiny-nl8,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-big-itc-tr,Helsinki-NLP,['bibtex']
google/multiberts-seed_0-step_0k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-large-ssm-nqo,google,['arxiv.org/abs/1910.10683']
pysentimiento/robertuito-pos,pysentimiento,[{'title': 'LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation'}]
kornosk/bert-political-election2020-twitter-mlm,kornosk,[{'title': 'Knowledge Enhanced Masked Language Model for Stance Detection'}]
iarfmoose/roberta-base-bulgarian,iarfmoose,['arxiv.org/abs/1907.11692']
Narrativa/mbart-large-50-finetuned-opus-pt-en-translation,Narrativa,['arxiv.org/abs/2008.00401']
espnet/english_male_ryanspeech_conformer_fastspeech2,espnet,[{'title': 'RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis'}]
espnet/kan-bayashi_ljspeech_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
mbartolo/roberta-large-synqa-ext,mbartolo,['arxiv.org/abs/2002.00293']
HooshvareLab/bert-fa-base-uncased-clf-digimag,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-en-el,Helsinki-NLP,['bibtex']
mrm8488/bert-mini-finetuned-squadv2,mrm8488,['arxiv.org/abs/1908.08962']
speechbrain/sepformer-wsj03mix,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
it5/it5-efficient-small-el32-news-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
qwant/fralbert-base,qwant,"['arxiv.org/abs/1909.11942', 'bibtex']"
l3cube-pune/mahahate-bert,l3cube-pune,['arxiv.org/abs/2203.13778']
razent/SciFive-large-PMC,razent,['arxiv.org/abs/2106.03598']
microsoft/cvt-w24-384-22k,microsoft,['arxiv.org/abs/2103.15808']
aihijo/transformers4ime-pinyingpt-concat,aihijo,"['arxiv.org/abs/2203.00249', {'title': 'Exploring and Adapting Chinese GPT to Pinyin Input Method'}]"
ScyKindness/Hatsune_Miku,ScyKindness,['arxiv.org/abs/1911.00536']
MoritzLaurer/MiniLM-L6-mnli-fever-docnli-ling-2c,MoritzLaurer,['arxiv.org/abs/2104.07179']
IIC/beto-base-spanish-squades,IIC,['arxiv.org/abs/2107.07253']
doc2query/msmarco-russian-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
MCG-NJU/videomae-base-short-ssv2,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
valurank/MiniLM-L6-Keyword-Extraction,valurank,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
google/roberta2roberta_L-24_gigaword,google,['arxiv.org/abs/1907.12461']
doc2query/msmarco-german-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
microsoft/cvt-13-384,microsoft,['arxiv.org/abs/2103.15808']
microsoft/swin-large-patch4-window12-384,microsoft,"['arxiv.org/abs/2103.14030', 'bibtex']"
google/t5-efficient-tiny-nl32,google,['arxiv.org/abs/2109.10686']
speechbrain/sepformer-libri3mix,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
Helsinki-NLP/opus-mt-tc-big-lt-en,Helsinki-NLP,['bibtex']
SkolkovoInstitute/Mutual_Implication_Score,SkolkovoInstitute,['bibtex']
microsoft/xclip-base-patch32-16-frames,microsoft,['arxiv.org/abs/2208.02816']
Helsinki-NLP/opus-mt-tc-big-bg-en,Helsinki-NLP,['bibtex']
PlanTL-GOB-ES/roberta-base-bne-capitel-pos,PlanTL-GOB-ES,"['arxiv.org/abs/1907.11692', 'bibtex']"
tals/albert-base-vitaminc-fever,tals,['bibtex']
espnet/kan-bayashi_csmsc_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Finnish-NLP/t5-base-nl36-finnish,Finnish-NLP,['arxiv.org/abs/1910.10683']
allenai/aspire-contextualsentence-singlem-biomed,allenai,['arxiv.org/abs/2111.08366']
nielsr/nt5-small-rc1,nielsr,"['arxiv.org/abs/2104.07307', 'bibtex']"
facebook/data2vec-vision-large,facebook,"['arxiv.org/abs/2202.03555', 'doi.org/10.48550/arxiv.2202.03555,']"
gchhablani/bert-base-cased-finetuned-mnli,gchhablani,['arxiv.org/abs/2105.03824']
castorini/afriberta_small,castorini,['bibtex']
model-attribution-challenge/gpt-j-6B,model-attribution-challenge,['arxiv.org/abs/2104.09864']
projecte-aina/roberta-base-ca-cased-tc,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
AdapterHub/bert-base-uncased-pf-squad_v2,AdapterHub,['bibtex']
microsoft/tapex-large-finetuned-wikisql,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
sentence-transformers/nli-bert-large-cls-pooling,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
espnet/english_male_ryanspeech_fastspeech,espnet,[{'title': 'RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis'}]
IIC/roberta-base-spanish-squades,IIC,['arxiv.org/abs/2107.07253']
Helsinki-NLP/opus-mt-tc-big-fa-gmq,Helsinki-NLP,['bibtex']
gsarti/it5-base-oscar,gsarti,"['arxiv.org/abs/2203.03759', {'title': 'IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation'}]"
google/bert_uncased_L-10_H-256_A-4,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
google/tapas-mini-finetuned-wtq,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
sentence-transformers/nli-bert-base-cls-pooling,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
kleinay/nominalization-candidate-classifier,kleinay,[{'title': 'QANom: Question-Answer driven SRL for Nominalizations'}]
gsarti/it5-large,gsarti,"['arxiv.org/abs/2203.03759', {'title': 'IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation'}]"
thu-coai/EVA2.0-large,thu-coai,"['arxiv.org/abs/2108.01547', {'title': 'EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training'}]"
google/t5-efficient-small-el32,google,['arxiv.org/abs/2109.10686']
lirondos/anglicisms-spanish-mbert,lirondos,['bibtex']
Hate-speech-CNERG/urdu-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
Geotrend/bert-base-pt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
m3hrdadfi/albert-fa-base-v2-sentiment-binary,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
facebook/wav2vec2-base-10k-voxpopuli-ft-es,facebook,['arxiv.org/abs/2101.00390']
ishan/distilbert-base-uncased-mnli,ishan,['arxiv.org/abs/1810.04805']
flair/ner-danish,flair,['bibtex']
IIC/marimari-r2r-mlsum,IIC,['arxiv.org/abs/1907.12461']
google/bert_uncased_L-10_H-768_A-12,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
navteca/all-mpnet-base-v2,navteca,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
ChainYo/segformer-sidewalk,ChainYo,['arxiv.org/abs/2105.15203']
mrm8488/longformer-base-4096-spanish,mrm8488,['arxiv.org/abs/2004.05150']
google/tapas-tiny-finetuned-wtq,google,"['arxiv.org/abs/2004.02349', 'bibtex']"
Graphcore/bert-large-uncased,Graphcore,['arxiv.org/abs/1904.00962']
it5/it5-base-question-answering,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Matthijs/deeplabv3_mobilenet_v2_1.0_513,Matthijs,"['arxiv.org/abs/1801.04381', {'title': 'Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation'}]"
espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2_2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
XLab/rst-intent-detection-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
GroNLP/T0pp-sharded,GroNLP,['arxiv.org/abs/2110.08207']
XLab/rst-sentiment-classification-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa,Helsinki-NLP,['bibtex']
razent/SciFive-large-Pubmed,razent,['arxiv.org/abs/2106.03598']
XLab/rst-gaokao-rc-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
XLab/rst-natural-language-inference-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
XLab/rst-information-extraction-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
XLab/rst-gaokao-writing-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
XLab/rst-gaokao-cloze-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
it5/it5-small-news-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
IIC/beto-base-spanish-sqac,IIC,['arxiv.org/abs/2107.07253']
izumi-lab/electra-small-japanese-discriminator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
XLab/rst-summarization-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
XLab/rst-fact-retrieval-11b,XLab,"['doi.org/10.24963/ijcai.2020/501)', {'title': 'reStructured Pre-training'}]"
l3cube-pune/hindi-marathi-dev-roberta,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
google/t5-xxl-ssm-nq,google,['arxiv.org/abs/1910.10683']
JonatanGk/roberta-base-ca-finetuned-cyberbullying-catalan,JonatanGk,['bibtex']
hfl/chinese-electra-180g-small-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
razent/cotext-2-cc,razent,['bibtex']
Xuandong/HPD-MiniLM-F128,Xuandong,"['arxiv.org/abs/2203.07687', {'title': 'Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation'}]"
facebook/tts_transformer-en-200_speaker-cv4,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
frgfm/repvgg_a0,frgfm,"['arxiv.org/abs/2101.03697', 'bibtex']"
north/t5_base_NCC_lm,north,['arxiv.org/abs/2104.09617']
AdapterHub/roberta-base-pf-boolq,AdapterHub,['bibtex']
DTAI-KULeuven/robbertje-1-gb-merged,DTAI-KULeuven,['arxiv.org/abs/2101.05716']
yuningm/bart-large-citesum-title,yuningm,['arxiv.org/abs/2205.06207']
facebook/wav2vec2-base-es-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
ncfrey/ChemGPT-4.7M,ncfrey,['bibtex']
KBLab/wav2vec2-large-voxrex,KBLab,['arxiv.org/abs/2205.03026']
vamsibanda/sbert-onnx-gtr-t5-xl,vamsibanda,['arxiv.org/abs/2112.07899']
canwenxu/BERT-of-Theseus-MNLI,canwenxu,['arxiv.org/abs/2002.02925']
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
rti-international/rota,rti-international,['doi.org/10.5281/zenodo.4770492)']
Geotrend/distilbert-base-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
morenolq/spotify-podcast-advertising-classification,morenolq,"['doi.org/10.1145/3477314.3507106},', 'bibtex']"
google/t5-3b-ssm-nq,google,['arxiv.org/abs/1910.10683']
swtx/ernie-3.0-base-chinese,swtx,['arxiv.org/abs/2106.02241']
Harveenchadha/vakyansh_hindi_base_pretrained,Harveenchadha,['arxiv.org/abs/2107.07402']
PrimeQA/squad-v1-xlm-roberta-large,PrimeQA,"['arxiv.org/abs/1911.02116', 'bibtex']"
sahita/lang-VoxLingua107-ecapa,sahita,[{'title': '{VoxLingua107'}]
Voicemod/fastspeech2-en-200_speaker-cv4,Voicemod,"['arxiv.org/abs/2006.04558', 'bibtex']"
projecte-aina/roberta-base-ca-v2-cased-tc,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
sentence-transformers/nli-bert-base-max-pooling,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
ai4bharat/MultiIndicQuestionGenerationSS,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
Salesforce/mixqg-3b,Salesforce,['arxiv.org/abs/2110.08175']
Langboat/mengzi-oscar-base,Langboat,['arxiv.org/abs/2110.06696']
hfl/chinese-legal-electra-small-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
Hate-speech-CNERG/hindi-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
wanyu/IteraTeR-PEGASUS-Revision-Generator,wanyu,['arxiv.org/abs/2203.03802']
projecte-aina/mbert-base-gencata,projecte-aina,['bibtex']
sentence-transformers/nli-bert-large,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
it5/it5-small-headline-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
pszemraj/led-base-16384-finetuned-booksum,pszemraj,['arxiv.org/abs/2105.08209']
tals/roberta_python,tals,"['arxiv.org/abs/2106.05784', {'title': 'Programming Puzzles'}]"
gchhablani/bert-base-cased-finetuned-mrpc,gchhablani,['arxiv.org/abs/2105.03824']
tner/roberta-large-tweetner7-2020,tner,['bibtex']
facebook/convnext-large-224-22k,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
doc2query/stackexchange-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
flax-community/gpt-neo-125M-apps-all,flax-community,['arxiv.org/abs/2107.03374']
Helsinki-NLP/opus-mt-tc-big-en-zle,Helsinki-NLP,['bibtex']
doc2query/reddit-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
xaqren/sentiment_analysis,xaqren,['arxiv.org/abs/1810.04805']
ELiRF/mt5-base-dacsa-es,ELiRF,"['arxiv.org/abs/2010.11934', 'bibtex']"
gokceuludogan/t2t-adeX-prompt,gokceuludogan,['bibtex']
sentence-transformers/xlm-r-base-en-ko-nli-ststb,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
hfl/rbt4,hfl,"['arxiv.org/abs/1906.08101', 'bibtex']"
facebook/data2vec-vision-large-ft1k,facebook,"['arxiv.org/abs/2202.03555', 'doi.org/10.48550/arxiv.2202.03555,']"
google/t5-efficient-tiny-dl8,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_3-step_2000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
microsoft/cvt-21-384,microsoft,['arxiv.org/abs/2103.15808']
FinanceInc/finbert-pretrain,FinanceInc,['arxiv.org/abs/2006.08097']
google/t5-11b-ssm-tqao,google,['arxiv.org/abs/1910.10683']
laituan245/molt5-large,laituan245,['arxiv.org/abs/2204.11817']
google/t5-3b-ssm-nqo,google,['arxiv.org/abs/1910.10683']
it5/it5-base-wiki-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
kinit/slovakbert-sentiment-twitter,kinit,"['arxiv.org/abs/2109.15254', 'bibtex']"
l3cube-pune/hate-roberta-hasoc-hindi,l3cube-pune,"['arxiv.org/abs/2110.12200', {'title': 'Hate and Offensive Speech Detection in Hindi and Marathi'}]"
ai4bharat/MultiIndicParaphraseGenerationSS,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
w11wo/sundanese-gpt2-base,w11wo,"['doi.org/10.21203/rs.3.rs-907893/v1}', 'bibtex']"
phiyodr/bert-large-finetuned-squad2,phiyodr,['arxiv.org/abs/1810.04805']
sentence-transformers/bert-large-nli-cls-token,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
yirmibesogluz/t2t-assert-ade-balanced,yirmibesogluz,['bibtex']
google/multiberts-seed_4-step_0k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
LiYuan/Amazon-Cup-Cross-Encoder-Regression,LiYuan,['doi.org/10.48550/arxiv.1908.10084.']
yirmibesogluz/t2t-ner-ade-balanced,yirmibesogluz,['bibtex']
w11wo/indo-roberta-small,w11wo,['arxiv.org/abs/1907.11692']
google/multiberts-seed_2-step_2000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
gchhablani/bert-base-cased-finetuned-rte,gchhablani,['arxiv.org/abs/2105.03824']
yuningm/bart-large-citesum,yuningm,['arxiv.org/abs/2205.06207']
csebuetnlp/banglishbert_generator,csebuetnlp,"['arxiv.org/abs/2101.00204', {'title': 'BanglaBERT: Lagnuage Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla'}]"
qanastek/pos-french-camembert,qanastek,"['arxiv.org/abs/1911.03894', 'bibtex']"
Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm,Finnish-NLP,['arxiv.org/abs/2111.09296']
ganchengguang/Roformer-base-japanese,ganchengguang,[{'title': 'Roformer: Enhanced transformer with rotary position embedding'}]
CLTL/gm-ner-xlmrbase,CLTL,['bibtex']
flax-community/gpt-neo-125M-apps,flax-community,['arxiv.org/abs/2107.03374']
asapp/sew-d-base-plus-400k-ft-ls100h,asapp,['arxiv.org/abs/2109.06870']
monsoon-nlp/no-phone-gpt2,monsoon-nlp,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
Helsinki-NLP/opus-mt-tc-big-en-ar,Helsinki-NLP,['bibtex']
cogint/in-boxbart,cogint,"['arxiv.org/abs/2204.07600', {'title': '{In-BoXBART: Get Instructions into Biomedical Multi-Task Learning'}]"
CarperAI/carptriever-1,CarperAI,"['arxiv.org/abs/2112.09118', {'title': 'The {P'}]"
gchhablani/bert-base-cased-finetuned-cola,gchhablani,['arxiv.org/abs/2105.03824']
google/t5-efficient-base-nl16,google,['arxiv.org/abs/2109.10686']
phiyodr/roberta-large-finetuned-squad2,phiyodr,['arxiv.org/abs/1907.11692']
brema76/vaccine_opinion_it,brema76,"['arxiv.org/abs/2207.12264', {'title': 'Dynamics of information flow and engaging power of narratives in the polarised debate on vaccines'}]"
usc-isi/sbert-roberta-large-anli-mnli-snli,usc-isi,['doi.org/10.18653/v1/D15-1075).']
speechbrain/sepformer-libri2mix,speechbrain,"['arxiv.org/abs/2010.13154', {'title': 'Attention is All You Need in Speech Separation'}]"
doc2query/msmarco-dutch-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
Sakonii/distilgpt2-nepali,Sakonii,['arxiv.org/abs/1911.02116']
nvidia/segformer-b0-finetuned-cityscapes-640-1280,nvidia,"['arxiv.org/abs/2105.15203', 'bibtex']"
gaetangate/bart-large_genrl_qald9,gaetangate,"['arxiv.org/abs/2108.07337', {'title': 'Generative relation linking for question answering over knowledge bases'}]"
Geotrend/bert-base-en-fr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Multilingual BERT'}]
moha/mbert_ar_c19,moha,['arxiv.org/abs/2004.04315']
nanopass/distilbert-base-uncased-emotion-2,nanopass,['arxiv.org/abs/1910.01108']
projecte-aina/roberta-base-ca-cased-ner,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
google/t5-efficient-xl-nl28,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-big-el-en,Helsinki-NLP,['bibtex']
google/t5-efficient-tiny-ff3000,google,['arxiv.org/abs/2109.10686']
UBC-NLP/AraT5-tweet-small,UBC-NLP,"['doi.org/10.14288/SOCKEYE).', 'bibtex']"
facebook/tts_transformer-vi-cv7,facebook,"['arxiv.org/abs/1809.08895', 'bibtex']"
l3cube-pune/marathi-roberta,l3cube-pune,['arxiv.org/abs/2202.01159']
tae898/emoberta-base,tae898,['arxiv.org/abs/2108.12009']
invokerliang/MWP-BERT-en,invokerliang,[{'title': 'MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving'}]
JiachengLi/uctopic-base,JiachengLi,"['arxiv.org/abs/2202.13469', {'title': 'UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining'}]"
doc2query/msmarco-portuguese-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
manueltonneau/bert-twitter-en-job-search,manueltonneau,['arxiv.org/abs/2203.09178']
facebook/xm_transformer_600m-ru_en-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
ixa-ehu/roberta-eus-euscrawl-base-cased,ixa-ehu,['arxiv.org/abs/2203.08111']
Nokia/nlgp-natural,Nokia,['arxiv.org/abs/2108.05198']
facebook/wav2vec2-base-de-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
mrm8488/deberta-v3-large-finetuned-mnli,mrm8488,['arxiv.org/abs/2006.03654']
tner/bertweet-large-tweetner7-continuous,tner,['bibtex']
ysakuramoto/mobilebert-ja,ysakuramoto,['arxiv.org/abs/2004.02984']
HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary,HooshvareLab,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
gchhablani/bert-base-cased-finetuned-qqp,gchhablani,['arxiv.org/abs/2105.03824']
swcrazyfan/Kingify-2Way-T5-Large-v1_1,swcrazyfan,[{'title': 'Kingify 2Way'}]
ThomasNLG/t5-weighter_cnndm-en,ThomasNLG,"['arxiv.org/abs/2103.12693', {'title': 'Questeval: Summarization asks for fact-based evaluation'}]"
camembert/camembert-base-ccnet-4gb,camembert,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
keshan/SinhalaBERTo,keshan,['arxiv.org/abs/1907.11692']
tner/roberta-large-tweetner7-selflabel2020-continuous,tner,['bibtex']
strombergnlp/dant5-small,strombergnlp,['arxiv.org/abs/2208.12097']
uer/chinese_roberta_L-2_H-512,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
witiko/mathberta,witiko,[{'title': 'Proceedings of the Working Notes of {CLEF'}]
HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg,HiTZ,"['arxiv.org/abs/2104.14690', 'bibtex']"
unicamp-dl/ptt5-large-t5-vocab,unicamp-dl,[{'title': 'PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data'}]
PrimeQA/mt5-base-tydi-question-generator,PrimeQA,[{'title': 'mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer'}]
coastalcph/fairlex-fscs-minilm,coastalcph,['bibtex']
McGill-NLP/bart-qg-mlquestions-backtraining,McGill-NLP,['bibtex']
sismetanin/sbert-ru-sentiment-rusentiment,sismetanin,['bibtex']
google/t5-efficient-tiny-nl24,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_1-step_2000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
yangheng/deberta-v3-large-absa,yangheng,"['arxiv.org/abs/2110.08604', 'bibtex']"
sentence-transformers/xlm-r-bert-base-nli-mean-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
north/t5_large_NCC,north,['arxiv.org/abs/2104.09617']
tner/roberta-large-tweetner7-2021,tner,['bibtex']
DTAI-KULeuven/robbertje-merged-dutch-sentiment,DTAI-KULeuven,[{'title': 'RobBERTje: A Distilled Dutch BERT Model'}]
l3cube-pune/marathi-albert,l3cube-pune,['arxiv.org/abs/2202.01159']
eml914/streaming_transformer_asr_librispeech,eml914,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
EventMiner/xlm-roberta-large-en-doc,EventMiner,['bibtex']
bigscience/T0_single_prompt,bigscience,['arxiv.org/abs/2110.08207']
HooshvareLab/bert-base-parsbert-peymaner-uncased,HooshvareLab,"['arxiv.org/abs/2005.12515', {'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]"
doc2query/msmarco-vietnamese-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
google/multiberts-seed_4-step_20k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Geotrend/distilbert-base-ru-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Davlan/afro-xlmr-base,Davlan,['arxiv.org/abs/2204.06487']
projecte-aina/roberta-base-ca-v2-cased-sts,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-da-ner,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
inokufu/bert-base-uncased-xnli-sts-finetuned-education,inokufu,['arxiv.org/abs/1810.04805']
l3cube-pune/marathi-bert,l3cube-pune,['arxiv.org/abs/2202.01159']
Geotrend/bert-base-en-fr-de-no-da-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/kan-bayashi_ljspeech_joint_train_conformer_fastspeech2_hifigan,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ncfrey/ChemGPT-1.2B,ncfrey,['bibtex']
facebook/regnet-y-1280-seer,facebook,['arxiv.org/abs/2202.08360']
google/multiberts-seed_8,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-3b-ssm,google,['arxiv.org/abs/1910.10683']
edbeeching/decision-transformer-gym-halfcheetah-medium,edbeeching,['arxiv.org/abs/2106.01345']
ncoop57/codeparrot-neo-125M-py,ncoop57,"['doi.org/10.5281/zenodo.5297715}', {'title': 'The Pile: An 800GB Dataset of Diverse Text for Language Modeling'}]"
tner/roberta-large-tweetner7-2020-selflabel2020-all,tner,['bibtex']
tner/bertweet-large-tweetner7-random,tner,['bibtex']
slone/mbart-large-51-myv-mul-v1,slone,['arxiv.org/abs/2209.09368']
Geotrend/distilbert-base-nl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
edbeeching/decision-transformer-gym-walker2d-medium-replay,edbeeching,['arxiv.org/abs/2106.01345']
imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,imdanboy,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
unicamp-dl/ptt5-small-t5-vocab,unicamp-dl,[{'title': 'PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data'}]
espnet/kan-bayashi_csj_asr_train_asr_transformer_raw_char_sp_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/t5-efficient-small-nl24,google,['arxiv.org/abs/2109.10686']
uer/roberta-large-wwm-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]"
flax-community/roberta-base-mr,flax-community,['arxiv.org/abs/1907.11692']
google/multiberts-seed_4-step_1000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-xxl-ssm-wqo,google,['arxiv.org/abs/1910.10683']
uer/roberta-medium-wwm-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]"
Muennighoff/SGPT-125M-weightedmean-msmarco-specb,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
tner/twitter-roberta-base-dec2021-tweetner7-all,tner,['bibtex']
google/t5-efficient-xl-nl16,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-big-pt-zle,Helsinki-NLP,['bibtex']
Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa,Intel,['arxiv.org/abs/2111.05754']
l3cube-pune/mahahate-multi-roberta,l3cube-pune,['arxiv.org/abs/2203.13778']
allenai/tk-instruct-3b-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
kornosk/bert-election2020-twitter-stance-biden,kornosk,[{'title': 'Knowledge Enhanced Masked Language Model for Stance Detection'}]
google/t5-xxl-ssm-nqo,google,['arxiv.org/abs/1910.10683']
CogComp/roberta-temporal-predictor,CogComp,['arxiv.org/abs/2202.00436']
facebook/s2t-small-covost2-en-fa-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
google/t5-efficient-base-nh24,google,['arxiv.org/abs/2109.10686']
Geotrend/bert-base-en-fr-es-de-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/kan-bayashi_csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/t5-efficient-base-nh16,google,['arxiv.org/abs/2109.10686']
google/t5-xxl-ssm-tqa,google,['arxiv.org/abs/1910.10683']
ai4bharat/MultiIndicWikiBioSS,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
google/t5-efficient-xl-nl12,google,['arxiv.org/abs/2109.10686']
izumi-lab/electra-base-japanese-generator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
Giyaseddin/distilroberta-base-finetuned-short-answer-assessment,Giyaseddin,['arxiv.org/abs/1806.02847']
nielsr/coref-bert-base,nielsr,['arxiv.org/abs/2004.06870']
espnet/kan-bayashi_jsut_full_band_vits_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
lirondos/anglicisms-spanish-flair-cs,lirondos,['bibtex']
BSC-TeMU/roberta-large-bne-capitel-ner,BSC-TeMU,['arxiv.org/abs/1907.11692']
ELiRF/mbart-large-cc25-dacsa-es,ELiRF,"['arxiv.org/abs/2001.08210', 'bibtex']"
Geotrend/distilbert-base-pt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
dbmdz/electra-base-turkish-mc4-uncased-discriminator,dbmdz,['doi.org/10.5281/zenodo.3770924}']
l3cube-pune/hing-mbert-mixed,l3cube-pune,['arxiv.org/abs/2204.08398']
thu-coai/EVA2.0-xlarge,thu-coai,"['arxiv.org/abs/2108.01547', {'title': 'EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training'}]"
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
google/fnet-large,google,"['arxiv.org/abs/2105.03824', 'bibtex']"
google/t5-xxl-ssm-tqao,google,['arxiv.org/abs/1910.10683']
PlanTL-GOB-ES/bsc-bio-ehr-es-cantemist,PlanTL-GOB-ES,['arxiv.org/abs/1907.11692']
google/bert_uncased_L-10_H-512_A-8,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
facebook/xm_transformer_600m-en_ru-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
csarron/mobilebert-uncased-squad-v1,csarron,['arxiv.org/abs/2004.02984']
PrimeQA/tydiqa-ft-listqa_nq-task-xlm-roberta-large,PrimeQA,"['arxiv.org/abs/1911.02116', 'bibtex']"
asapp/sew-small-100k,asapp,['arxiv.org/abs/2109.06870']
l3cube-pune/hindi-bert-scratch,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
Hate-speech-CNERG/dehatebert-mono-italian,Hate-speech-CNERG,"['arxiv.org/abs/2004.06465', {'title': 'Deep Learning Models for Multilingual Hate Speech Detection'}]"
uer/pegasus-large-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'Pegasus: Pre-training with extracted gap-sentences for abstractive summarization'}]"
HUPD/hupd-t5-small,HUPD,"[{'title': 'The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications'}]"
benjamin/gpt2-wechsel-chinese,benjamin,['bibtex']
google/t5-efficient-small-nl36,google,['arxiv.org/abs/2109.10686']
google/ddpm-ema-cat-256,google,['arxiv.org/abs/2006.11239']
AdapterHub/roberta-base-pf-squad,AdapterHub,['bibtex']
google/t5-efficient-large-dm256,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-tiny-nh16,google,['arxiv.org/abs/2109.10686']
it5/it5-efficient-small-el32-headline-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
shahrukhx01/gbert-hasoc-german-2019,shahrukhx01,"['doi.org/10.1145/3368567.3368584},', 'bibtex']"
Geotrend/distilbert-base-en-fr-es-de-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
amine/bert-base-5lang-cased,amine,[{'title': 'Load What You Need: Smaller Versions of Multilingual BERT'}]
manueltonneau/bert-twitter-pt-job-offer,manueltonneau,['arxiv.org/abs/2203.09178']
IDEA-CCNL/Randeng-Transformer-1.1B-Denoise,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
alpineai/cosql,alpineai,"['arxiv.org/abs/2109.05093', 'bibtex']"
it5/it5-efficient-small-el32-wiki-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
HiTZ/A2T_RoBERTa_SMFA_ACE-arg_WikiEvents-arg,HiTZ,"['arxiv.org/abs/2104.14690', 'bibtex']"
tau/bart-large-sled,tau,"['arxiv.org/abs/2208.00748', {'title': 'Efficient Long-Text Understanding with Short-Text Models'}]"
speechbrain/SLU-direct-SLURP-hubert-enc,speechbrain,['arxiv.org/abs/2011.13205']
Narrativa/mbart-large-50-finetuned-opus-en-pt-translation,Narrativa,['arxiv.org/abs/2008.00401']
Helsinki-NLP/opus-mt-tc-big-ar-itc,Helsinki-NLP,['bibtex']
ctoraman/RoBERTa-TR-medium-char,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
google/t5-efficient-large-nl20,google,['arxiv.org/abs/2109.10686']
facebook/levit-128,facebook,['arxiv.org/abs/2104.01136']
google/multiberts-seed_4-step_80k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-tiny-nl2,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-tiny-dl2,google,['arxiv.org/abs/2109.10686']
ethzanalytics/ai-msgbot-gpt2-XL-dialogue,ethzanalytics,['bibtex']
nvidia/tts_hifigan,nvidia,['arxiv.org/abs/2010.05646']
julien-c/kan-bayashi_csmsc_tacotron2,julien-c,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
uer/chinese_roberta_L-8_H-768,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
google/multiberts-seed_4-step_500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
kornosk/bert-election2020-twitter-stance-trump,kornosk,[{'title': 'Knowledge Enhanced Masked Language Model for Stance Detection'}]
madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1,madlag,['arxiv.org/abs/2005.07683']
rufimelo/Legal-BERTimbau-large,rufimelo,['bibtex']
uer/chinese_roberta_L-8_H-128,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
sentence-transformers/paraphrase-albert-base-v2,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
gaetangate/bart-large_genrl_lcquad1,gaetangate,"['arxiv.org/abs/2108.07337', {'title': 'Generative relation linking for question answering over knowledge bases'}]"
sismetanin/sbert-ru-sentiment-rureviews,sismetanin,['bibtex']
nielsr/coref-roberta-base,nielsr,['arxiv.org/abs/2004.06870']
facebook/wav2vec2-base-10k-voxpopuli-ft-cs,facebook,['arxiv.org/abs/2101.00390']
Hate-speech-CNERG/hindi-codemixed-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
unicamp-dl/ptt5-base-t5-vocab,unicamp-dl,[{'title': 'PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data'}]
HannahRoseKirk/Hatemoji,HannahRoseKirk,['arxiv.org/abs/2108.05921']
leonardvorbeck/wav2vec2-large-robust-LS960,leonardvorbeck,['arxiv.org/abs/2104.01027']
laituan245/molt5-small-smiles2caption,laituan245,['arxiv.org/abs/2204.11817']
RUCAIBox/mvp-open-dialog,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
allenai/tailor,allenai,['arxiv.org/abs/2107.07150']
classla/bcms-bertic-parlasent-bcs-ter,classla,"['arxiv.org/abs/2206.00929', 'doi.org/10.48550/arxiv.2206.00929,', 'bibtex']"
google/multiberts-seed_0-step_500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
sentence-transformers/distilbert-base-nli-max-tokens,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
google/t5-efficient-large-nh24,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-big-fi-zls,Helsinki-NLP,['bibtex']
noahkim/KoBigBird-KoBart-EncoderDecoderModel,noahkim,['doi.org/10.5281/zenodo.5654154}']
tner/twitter-roberta-base-2019-90m-tweetner7-all,tner,['bibtex']
Helsinki-NLP/opus-mt-tc-big-en-lt,Helsinki-NLP,['bibtex']
google/t5-11b-ssm-wq,google,['arxiv.org/abs/1910.10683']
google/tapas-tiny-finetuned-tabfact,google,"['arxiv.org/abs/2010.00571', {'title': 'TabFact : A Large-scale Dataset for Table-based Fact Verification'}]"
sebastian-hofstaetter/idcm-distilbert-msmarco_doc,sebastian-hofstaetter,"['arxiv.org/abs/2105.09816', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-fa-itc,Helsinki-NLP,['bibtex']
addy88/eli5-all-mpnet-base-v2,addy88,"['arxiv.org/abs/1908.10084', 'bibtex']"
BSC-TeMU/roberta-large-bne-capitel-pos,BSC-TeMU,['arxiv.org/abs/1907.11692']
facebook/convnext-large-384,facebook,"['arxiv.org/abs/2201.03545', 'bibtex']"
facebook/wav2vec2-base-10k-voxpopuli-ft-de,facebook,['arxiv.org/abs/2101.00390']
facebook/levit-384,facebook,['arxiv.org/abs/2104.01136']
google/t5-xxl-ssm-wq,google,['arxiv.org/abs/1910.10683']
torchxrayvision/resnet50-res512-all,torchxrayvision,"['arxiv.org/abs/2111.00595', 'bibtex']"
google/multiberts-seed_4-step_100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
bookbot/wav2vec2-adult-child-cls,bookbot,['arxiv.org/abs/2006.11477']
google/bert_uncased_L-10_H-128_A-2,google,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
google/t5-efficient-xl-nl2,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-xxl-nl4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-tiny-nl16,google,['arxiv.org/abs/2109.10686']
microsoft/tapex-base-finetuned-tabfact,microsoft,"['arxiv.org/abs/2107.07653', {'title': '{TAPEX'}]"
google/t5-efficient-tiny-ff9000,google,['arxiv.org/abs/2109.10686']
keras-io/deeplabv3p-resnet50,keras-io,['arxiv.org/abs/1811.12596']
domenicrosati/question_converter-3b,domenicrosati,"[{'title': ""Can NLI Models Verify QA Systems' Predictions?""}]"
facebook/s2t-wav2vec2-large-en-ca,facebook,"['arxiv.org/abs/2104.06678', 'bibtex']"
Matthijs/mobilevit-small,Matthijs,"['arxiv.org/abs/2110.02178', {'title': 'MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer'}]"
facebook/wav2vec2-base-fr-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
flair/frame-english-fast,flair,[{'title': 'FLAIR: An easy-to-use framework for state-of-the-art NLP'}]
kz/mt5base-finetuned-ECC-japanese-small,kz,['arxiv.org/abs/2201.11903']
google/t5-efficient-tiny-el2,google,['arxiv.org/abs/2109.10686']
coastalcph/fairlex-scotus-minilm,coastalcph,['bibtex']
uer/roberta-small-word-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]"
nghuyong/ernie-3.0-micro-zh,nghuyong,"['arxiv.org/abs/2107.02137', {'title': 'Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation'}]"
google/t5-efficient-tiny-el12,google,['arxiv.org/abs/2109.10686']
lincoln/barthez-squadFR-fquad-piaf-question-generation,lincoln,"['arxiv.org/abs/2010.12321', {'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]"
it5/it5-small-question-answering,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Langboat/mengzi-oscar-base-caption,Langboat,['arxiv.org/abs/2110.06696']
Helsinki-NLP/opus-mt-tc-big-zle-zle,Helsinki-NLP,['bibtex']
bigscience/T0_original_task_only,bigscience,['arxiv.org/abs/2110.08207']
google/t5-efficient-tiny-ff6000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-tiny-nl12,google,['arxiv.org/abs/2109.10686']
AdapterHub/bert-base-uncased-pf-social_i_qa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
doc2query/msmarco-french-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
doc2query/yahoo_answers-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
google/t5-efficient-tiny-dl6,google,['arxiv.org/abs/2109.10686']
ratishsp/Centrum-multinews,ratishsp,['arxiv.org/abs/2208.01006']
espnet/kan-bayashi_vctk_full_band_multi_spk_vits,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tner/roberta-large-tweetner7-random,tner,['bibtex']
google/multiberts-seed_4-step_40k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
m3hrdadfi/albert-fa-base-v2-ner-arman,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
razent/spbert-mlm-zero,razent,['arxiv.org/abs/2106.09997']
IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-CWS-Chinese,IDEA-CCNL,"['arxiv.org/abs/2209.02970', 'bibtex']"
RUCAIBox/mtl-open-dialog,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
google/tapas-small-finetuned-tabfact,google,"['arxiv.org/abs/2010.00571', {'title': 'TabFact : A Large-scale Dataset for Table-based Fact Verification'}]"
tner/twitter-roberta-base-dec2021-tweetner7-2021,tner,['bibtex']
sismetanin/xlm_roberta_large-ru-sentiment-rureviews,sismetanin,['bibtex']
google/t5-efficient-large-dm128,google,['arxiv.org/abs/2109.10686']
aodiniz/bert_uncased_L-10_H-512_A-8_cord19-200616_squad2,aodiniz,['arxiv.org/abs/1908.08962']
google/t5-efficient-tiny-nh32,google,['arxiv.org/abs/2109.10686']
allenai/wmt16-en-de-12-1,allenai,['arxiv.org/abs/2006.10369']
sabhi/t5-base-qa-qg,sabhi,['arxiv.org/abs/1910.10683']
dbmdz/electra-base-turkish-mc4-uncased-generator,dbmdz,['doi.org/10.5281/zenodo.3770924}']
google/t5-efficient-mini-nl8,google,['arxiv.org/abs/2109.10686']
manueltonneau/bert-twitter-en-is-hired,manueltonneau,['arxiv.org/abs/2203.09178']
ontocord/fastspeech2-en,ontocord,['arxiv.org/abs/2006.04558']
Finnish-NLP/t5-small-nl24-finnish,Finnish-NLP,['arxiv.org/abs/1910.10683']
browndw/docusco-bert,browndw,"['arxiv.org/abs/1810.04805', 'bibtex']"
google/t5-efficient-tiny-el8,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-nh8,google,['arxiv.org/abs/2109.10686']
bvanaken/CORe-clinical-mortality-prediction,bvanaken,['bibtex']
projecte-aina/roberta-base-ca-cased-te,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
google/t5-efficient-tiny-ff12000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-tiny-nh8,google,['arxiv.org/abs/2109.10686']
frgfm/darknet19,frgfm,"['arxiv.org/abs/1612.08242', 'bibtex']"
espnet/kan-bayashi_ljspeech_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/bert-base-15lang-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Multilingual BERT'}]
chkla/parlbert-topic-german,chkla,[{'title': 'FrameASt: A Framework for Second-level Agenda Setting in Parliamentary Debates through the Lense of Comparative Agenda Topics'}]
nvidia/stt_rw_conformer_ctc_large,nvidia,['arxiv.org/abs/2005.08100']
CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
google/t5-efficient-xl-nl8,google,['arxiv.org/abs/2109.10686']
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
google/multiberts-seed_3-step_20k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-small-nl48,google,['arxiv.org/abs/2109.10686']
p208p2002/t5-squad-nqg-hl,p208p2002,['arxiv.org/abs/1606.05250']
flax-sentence-embeddings/reddit_single-context_mpnet-base,flax-sentence-embeddings,['arxiv.org/abs/1904.06472']
google/multiberts-seed_3-step_500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Helsinki-NLP/opus-mt-tc-big-es-zle,Helsinki-NLP,['bibtex']
google/multiberts-seed_4-step_60k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
mlaricheva/roberta-psych,mlaricheva,['arxiv.org/abs/2208.06525']
nvidia/stt_fr_conformer_ctc_large,nvidia,['arxiv.org/abs/2005.08100']
google/multiberts-seed_3-step_40k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
north/byt5_base_NCC,north,['arxiv.org/abs/2104.09617']
abdouaziiz/soraberta,abdouaziiz,['arxiv.org/abs/1907.11692']
Helsinki-NLP/opus-mt-tc-big-it-zle,Helsinki-NLP,['bibtex']
keras-io/vit_small_ds_v2,keras-io,['arxiv.org/abs/2010.11929']
Helsinki-NLP/opus-mt-tc-big-en-ces_slk,Helsinki-NLP,['bibtex']
yangheng/deberta-v3-base-absa,yangheng,"['arxiv.org/abs/2110.08604', 'bibtex']"
l3cube-pune/hing-roberta,l3cube-pune,['arxiv.org/abs/2204.08398']
osanseviero/asr-with-transformers-wav2vec2,osanseviero,['arxiv.org/abs/2006.11477']
ptaszynski/bert-base-polish-cyberbullying,ptaszynski,[{'title': 'Polish BERT trained for Automatic Cyberbullying Detection'}]
google/t5-efficient-base-nh32,google,['arxiv.org/abs/2109.10686']
Harveenchadha/vakyansh-wav2vec2-tamil-tam-250,Harveenchadha,['arxiv.org/abs/2107.07402']
tner/roberta-large-tweetner7-2020-selflabel2021-all,tner,['bibtex']
saahith/wav2vec2_base_100h_ngram,saahith,['arxiv.org/abs/2006.11477']
google/t5-11b-ssm-nqo,google,['arxiv.org/abs/1910.10683']
google/multiberts-seed_0-step_900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
espnet/farsi_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Kamrani/t5-large,Kamrani,"['arxiv.org/abs/1805.12471', 'bibtex']"
google/multiberts-seed_0-step_1100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-small-nl2,google,['arxiv.org/abs/2109.10686']
AdapterHub/bert-base-uncased-pf-rotten_tomatoes,AdapterHub,['bibtex']
espnet/kan-bayashi_ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_-truncated-737899,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/multiberts-seed_3-step_60k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/xm_transformer_600m-fr_en-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
jkang/espnet2_librispeech_100_conformer,jkang,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ctu-aic/xlm-roberta-large-xnli-csfever,ctu-aic,"['arxiv.org/abs/2201.11115', 'bibtex']"
apol/dalle-mini,apol,['arxiv.org/abs/1910.13461']
keras-io/conv_mixer_image_classification,keras-io,['arxiv.org/abs/2201.09792']
uer/roberta-mini-wwm-chinese-cluecorpussmall,uer,"['arxiv.org/abs/1909.05658', {'title': 'UER: An Open-Source Toolkit for Pre-training Models'}]"
tner/deberta-v3-large-fin,tner,['bibtex']
chizhikchi/Spanish_disease_finder,chizhikchi,[{'title': 'SINAI at CLEF 2022: Leveraging biomedical transformers to detect and normalize disease mentions'}]
google/multiberts-seed_3-step_0k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-small-el48,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-dl2,google,['arxiv.org/abs/2109.10686']
facebook/levit-192,facebook,['arxiv.org/abs/2104.01136']
mrm8488/mobilebert-uncased-finetuned-squadv1,mrm8488,['arxiv.org/abs/2004.02984']
laituan245/molt5-large-caption2smiles,laituan245,['arxiv.org/abs/2204.11817']
munggok/roberta-base-indo,munggok,['arxiv.org/abs/1907.11692']
manueltonneau/bert-twitter-pt-job-search,manueltonneau,['arxiv.org/abs/2203.09178']
ctu-aic/xlm-roberta-large-squad2-ctkfacts,ctu-aic,"['arxiv.org/abs/2201.11115', 'bibtex']"
xfbai/AMRBART-large-finetuned-AMR3.0-AMR2Text,xfbai,['bibtex']
Helsinki-NLP/opus-mt-tc-big-zls-itc,Helsinki-NLP,['bibtex']
tner/twitter-roberta-base-2019-90m-tweetner7-continuous,tner,['bibtex']
flax-community/RoBERTa-large-finnish,flax-community,['arxiv.org/abs/1907.11692']
north/t5_large_NCC_lm,north,['arxiv.org/abs/2104.09617']
facebook/xm_transformer_600m-en_es-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
google/t5-efficient-base-dl6,google,['arxiv.org/abs/2109.10686']
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
benjamin/gpt2-wechsel-french,benjamin,['bibtex']
ufal/byt5-small-multilexnorm2021-trde,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
sismetanin/xlm_roberta_base-ru-sentiment-rureviews,sismetanin,['bibtex']
tner/bertweet-large-tweetner7-2020,tner,['bibtex']
google/multiberts-seed_0-step_1000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
tugstugi/bert-base-mongolian-uncased,tugstugi,['arxiv.org/abs/1810.04805']
rufimelo/Legal-BERTimbau-sts-large,rufimelo,['bibtex']
facebook/regnet-y-1280-seer-in1k,facebook,['arxiv.org/abs/2202.08360']
google/t5-efficient-base-nl8,google,['arxiv.org/abs/2109.10686']
tner/bertweet-large-tweetner7-2021,tner,['bibtex']
dvm1983/TinyBERT_General_4L_312D_de,dvm1983,['arxiv.org/abs/1909.10351']
tals/albert-xlarge-vitaminc,tals,['bibtex']
kornosk/bert-election2020-twitter-stance-trump-KE-MLM,kornosk,[{'title': 'Knowledge Enhanced Masked Language Model for Stance Detection'}]
google/t5-efficient-base-ff6000,google,['arxiv.org/abs/2109.10686']
CAiRE/wav2vec2-large-xlsr-53-cantonese,CAiRE,[{'title': 'ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation'}]
google/ddpm-church-256,google,['arxiv.org/abs/2006.11239']
Hate-speech-CNERG/malayalam-codemixed-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
google/t5-11b-ssm,google,['arxiv.org/abs/1910.10683']
google/multiberts-seed_0-step_80k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_60k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
mrm8488/squeezebert-finetuned-squadv2,mrm8488,['arxiv.org/abs/2006.11316']
google/multiberts-seed_0-step_20k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-mini,google,['arxiv.org/abs/2109.10686']
Kalindu/SinBerto,Kalindu,['arxiv.org/abs/1907.11692']
Tejas21/Totto_t5_base_BLEURT_24k_steps,Tejas21,['arxiv.org/abs/2004.04696']
l3cube-pune/marathi-gpt,l3cube-pune,"['arxiv.org/abs/2202.01159', {'title': 'L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources'}]"
nvidia/stt_de_conformer_ctc_large,nvidia,['arxiv.org/abs/2005.08100']
ychenNLP/arabic-ner-ace,ychenNLP,['bibtex']
razent/cotext-1-cc,razent,['bibtex']
google/multiberts-seed_0-step_700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
RVN/XLMR-BERTovski,RVN,['bibtex']
Helsinki-NLP/opus-mt-tc-base-uk-fi,Helsinki-NLP,['bibtex']
google/multiberts-seed_2-step_1900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/s2t-small-covost2-fr-en-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
doc2query/stackexchange-title-body-t5-small-v1,doc2query,['arxiv.org/abs/1904.08375']
google/t5-efficient-large-dl4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-tiny-ff2000,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_3-step_100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-large-dl2,google,['arxiv.org/abs/2109.10686']
google/t5-xxl-ssm,google,['arxiv.org/abs/1910.10683']
sshleifer/bb3b-tok,sshleifer,"['arxiv.org/abs/1907.06616', 'bibtex']"
dmrau/bow-bert,dmrau,[{'title': 'The Role of Complex NLP in Transformers for Text Ranking?'}]
lcampillos/roberta-es-clinical-trials-ner,lcampillos,"['arxiv.org/abs/1910.09700', {'title': 'A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine'}]"
Helsinki-NLP/opus-mt-tc-base-fi-uk,Helsinki-NLP,['bibtex']
google/t5-efficient-large-el8,google,['arxiv.org/abs/2109.10686']
lordtt13/t5-inshorts,lordtt13,['arxiv.org/abs/1910.10683']
rufimelo/Legal-BERTimbau-base,rufimelo,['bibtex']
google/t5-efficient-small-el64,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-kv128,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_1-step_1600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Helsinki-NLP/opus-mt-tc-base-zle-bat,Helsinki-NLP,['bibtex']
doc2query/stackexchange-title-body-t5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
google/multiberts-seed_4-step_140k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
laituan245/molt5-small-caption2smiles,laituan245,['arxiv.org/abs/2204.11817']
Intel/bert-large-uncased-squadv1.1-sparse-80-1x4-block-pruneofa,Intel,['arxiv.org/abs/2111.05754']
google/multiberts-seed_0-step_200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
relbert/roberta-large-conceptnet-average-no-mask-prompt-d-nce,relbert,['bibtex']
cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020,cardiffnlp,['bibtex']
google/multiberts-seed_3-step_80k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-base-nl36,google,['arxiv.org/abs/2109.10686']
ganchengguang/RoBERTa-base-japanese-sentencepiece,ganchengguang,[{'title': 'Roberta: A robustly optimized bert pretraining approach'}]
UMCU/MedRoBERTa.nl_NegationDetection,UMCU,"['arxiv.org/abs/2209.00470', 'doi.org/10.5281/zenodo.6980076']"
google/t5-efficient-small-el2,google,['arxiv.org/abs/2109.10686']
xfbai/AMRBART-large-finetuned-AMR2.0-AMRParsing,xfbai,['bibtex']
google/t5-efficient-base-dl8,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-dl16,google,['arxiv.org/abs/2109.10686']
mrm8488/spanbert-base-finetuned-squadv2,mrm8488,['arxiv.org/abs/1907.10529']
projecte-aina/roberta-base-ca-cased-sts,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
mlcorelib/deberta-base-uncased,mlcorelib,"['arxiv.org/abs/1810.04805', 'bibtex']"
google/t5-efficient-large-dl12,google,['arxiv.org/abs/2109.10686']
beatrice-portelli/DiLBERT,beatrice-portelli,[{'title': '{DilBERT'}]
AdapterHub/roberta-base-pf-qqp,AdapterHub,['bibtex']
google/t5-efficient-base-nl32,google,['arxiv.org/abs/2109.10686']
neuralmagic/bert-large-uncased-finetuned-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
ufal/byt5-small-multilexnorm2021-en,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
google/multiberts-seed_0-step_1300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-base-el8,google,['arxiv.org/abs/2109.10686']
facebook/wav2vec2-xls-r-2b-en-to-15,facebook,['arxiv.org/abs/2111.09296']
kornosk/polibertweet-political-twitter-roberta-mlm,kornosk,[{'title': 'PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter'}]
manueltonneau/bert-twitter-en-job-offer,manueltonneau,['arxiv.org/abs/2203.09178']
lighteternal/nli-xlm-r-greek,lighteternal,['arxiv.org/abs/1908.10084']
tner/twitter-roberta-base-dec2021-tweetner7-2020,tner,['bibtex']
SenseTime/deformable-detr-with-box-refine,SenseTime,"['arxiv.org/abs/2010.04159', 'doi.org/10.48550/arxiv.2010.04159,']"
eugenesiow/pan,eugenesiow,['arxiv.org/abs/2010.01073']
it5/it5-efficient-small-el32-question-answering,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
AdapterHub/bert-base-uncased-pf-boolq,AdapterHub,['bibtex']
AdapterHub/roberta-base-pf-anli_r3,AdapterHub,['bibtex']
llangnickel/long-covid-classification,llangnickel,"['doi.org/10.1093/database/baac048},', 'bibtex']"
ganchengguang/RoBERTa-base-janpanese,ganchengguang,[{'title': 'Roberta: A robustly optimized bert pretraining approach'}]
Helsinki-NLP/opus-mt-tc-big-et-en,Helsinki-NLP,['bibtex']
google/ddpm-ema-bedroom-256,google,['arxiv.org/abs/2006.11239']
google/multiberts-seed_2-step_900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/wav2vec2-base-10k-voxpopuli-ft-fi,facebook,['arxiv.org/abs/2101.00390']
torchxrayvision/densenet121-res224-all,torchxrayvision,"['arxiv.org/abs/2002.02497', 'bibtex']"
google/multiberts-seed_3-step_1000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
eugenesiow/drln,eugenesiow,['arxiv.org/abs/1906.12021']
north/t5_small_NCC,north,['arxiv.org/abs/2104.09617']
AIDA-UPM/BERTuit-base,AIDA-UPM,['arxiv.org/abs/2204.03465']
Sakonii/distilbert-base-nepali,Sakonii,['arxiv.org/abs/1911.02116']
facebook/regnet-x-040,facebook,['arxiv.org/abs/2003.13678']
RUCAIBox/mvp-multi-task,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
google/t5-efficient-tiny-nh1,google,['arxiv.org/abs/2109.10686']
mbeukman/xlm-roberta-base-finetuned-ner-luganda,mbeukman,['arxiv.org/abs/2103.11811']
praf-choub/bart-CaPE-xsum,praf-choub,"['arxiv.org/abs/2110.07166', 'doi.org/10.48550/arxiv.2110.07166,']"
facebook/s2t-wav2vec2-large-en-ar,facebook,"['arxiv.org/abs/2104.06678', 'bibtex']"
Geotrend/bert-base-en-es-pt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
google/t5-efficient-small-el8-dl4,google,['arxiv.org/abs/2109.10686']
doc2query/reddit-t5-small-v1,doc2query,['arxiv.org/abs/1904.08375']
Helsinki-NLP/opus-mt-tc-base-hu-uk,Helsinki-NLP,['bibtex']
eugenesiow/a2n,eugenesiow,['arxiv.org/abs/2104.09497']
flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
w11wo/indonesian-roberta-base-indonli,w11wo,['arxiv.org/abs/1907.11692']
google/t5-efficient-large-nl10,google,['arxiv.org/abs/2109.10686']
AdapterHub/roberta-base-pf-imdb,AdapterHub,['bibtex']
google/t5-efficient-small-el8-dl2,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_0-step_1800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-base-ff1000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-el4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-el8-dl1,google,['arxiv.org/abs/2109.10686']
Intel/bert-base-uncased-sparse-80-1x4-block-pruneofa,Intel,['arxiv.org/abs/2111.05754']
google/t5-efficient-small-nl20,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-mini-nl24,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-nl22,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-dm512,google,['arxiv.org/abs/2109.10686']
superb/wav2vec2-large-superb-er,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
google/t5-efficient-base-kv16,google,['arxiv.org/abs/2109.10686']
omarxadel/hubert-large-arabic-egyptian,omarxadel,['arxiv.org/abs/2106.07447']
google/t5-efficient-large-nl16,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-dl4,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_0-step_160k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_40k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
ctoraman/RoBERTa-TR-medium-word-44k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
Helsinki-NLP/opus-mt-tc-big-ces_slk-en,Helsinki-NLP,['bibtex']
aapot/wav2vec2-xlsr-1b-finnish-v2,aapot,['arxiv.org/abs/2111.09296']
google/multiberts-seed_4-step_1600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
EleutherAI/enformer-191k,EleutherAI,['doi.org/10.1038/s41592-021-01252-x']
google/t5-efficient-small-el8,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-xl-nl4,google,['arxiv.org/abs/2109.10686']
StevenLimcorn/indo-roberta-indonli,StevenLimcorn,['bibtex']
taln-ls2n/POET,taln-ls2n,"['arxiv.org/abs/1911.03894', 'bibtex']"
google/multiberts-seed_0-step_1700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
RUCAIBox/mvp-summarization,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
google/t5-efficient-base-el16,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-dl32,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_0-step_300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_1500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_4-step_120k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Helsinki-NLP/opus-mt-tc-big-gmq-zle,Helsinki-NLP,['bibtex']
google/multiberts-seed_0-step_1600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-base-dm256,google,['arxiv.org/abs/2109.10686']
w11wo/javanese-bert-small,w11wo,"['arxiv.org/abs/1810.04805', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
Geotrend/bert-base-en-de-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
google/t5-efficient-small-nl8,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-nh2,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-nl8,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-kv128,google,['arxiv.org/abs/2109.10686']
mrm8488/xlm-multi-finetuned-xquadv1,mrm8488,"['arxiv.org/abs/1901.07291', 'bibtex']"
it5/it5-efficient-small-el32-question-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
google/t5-efficient-large-el4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-el16-dl1,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-kv256,google,['arxiv.org/abs/2109.10686']
sismetanin/rubert_conversational-ru-sentiment-rureviews,sismetanin,['bibtex']
google/t5-efficient-small-nl32,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-base-uk-hu,Helsinki-NLP,['bibtex']
timm/eca_nfnet_l0,timm,"['arxiv.org/abs/2102.06171', 'bibtex']"
google/t5-efficient-base-kv256,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-base-tr-uk,Helsinki-NLP,['bibtex']
classla/bcms-bertic-frenk-hate,classla,"['arxiv.org/abs/1906.02045', 'bibtex']"
google/multiberts-seed_1-step_180k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-large-nl4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-nl2,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-nl16,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-nl2,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_2-step_400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-large-nl12,google,['arxiv.org/abs/2109.10686']
Helsinki-NLP/opus-mt-tc-big-zle-zls,Helsinki-NLP,['bibtex']
google/multiberts-seed_0-step_1900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
it5/it5-base-question-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
google/multiberts-seed_1-step_0k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-large-dl6,google,['arxiv.org/abs/2109.10686']
gealexandri/palobert-base-greek-uncased-v1,gealexandri,['arxiv.org/abs/1907.11692']
flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
Helsinki-NLP/opus-mt-tc-big-itc-eu,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-itc-bat,Helsinki-NLP,['bibtex']
tner/twitter-roberta-base-dec2020-tweetner7-continuous,tner,['bibtex']
tner/bert-large-tweetner7-continuous,tner,['bibtex']
facebook/wav2vec2-conformer-rel-pos-large-100h-ft,facebook,['arxiv.org/abs/2010.05171']
google/t5-efficient-base-ff2000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-nl4,google,['arxiv.org/abs/2109.10686']
xyma/PROP-wiki,xyma,"['doi.org/10.1145/3437963.3441777},', 'bibtex']"
google/multiberts-seed_3-step_1100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
it5/it5-small-question-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Helsinki-NLP/opus-mt-tc-big-fi-zle,Helsinki-NLP,['bibtex']
google/t5-efficient-large-nh8,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_1-step_1100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
EleutherAI/enformer-191k_corr_coef_obj,EleutherAI,['doi.org/10.1038/s41592-021-01252-x']
google/t5-efficient-mini-nl12,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-el12,google,['arxiv.org/abs/2109.10686']
AdapterHub/roberta-base-pf-squad_v2,AdapterHub,['bibtex']
google/t5-efficient-large-dm768,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-el4,google,['arxiv.org/abs/2109.10686']
p208p2002/t5-squad-qg-hl,p208p2002,['arxiv.org/abs/1606.05250']
google/t5-efficient-large-dm512,google,['arxiv.org/abs/2109.10686']
ForutanRad/bert-fa-QA-v1,ForutanRad,['arxiv.org/abs/2005.12515']
lightonai/RITA_l,lightonai,"['arxiv.org/abs/2205.05789', {'title': 'RITA: a Study on Scaling Up Generative Protein Sequence Models'}]"
speechbrain/resepformer-wsj02mix,speechbrain,['arxiv.org/abs/2206.09507']
google/t5-efficient-base-dm2000,google,['arxiv.org/abs/2109.10686']
projecte-aina/roberta-base-ca-v2-cased-ner,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
AdapterHub/roberta-base-pf-cola,AdapterHub,['bibtex']
google/t5-efficient-large-dl8,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-kv16,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_4-step_1900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-base-nl48,google,['arxiv.org/abs/2109.10686']
qarib/bert-base-qarib60_860k,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
google/t5-efficient-large-nh12,google,['arxiv.org/abs/2109.10686']
qiaoyi/Comment_Summarization4DesignTutor,qiaoyi,['arxiv.org/abs/1805.12471']
google/t5-efficient-base-nl4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-kv32,google,['arxiv.org/abs/2109.10686']
w11wo/javanese-bert-small-imdb-classifier,w11wo,"['arxiv.org/abs/1810.04805', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
google/t5-efficient-base-kv32,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-ff9000,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_4-step_1300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-small-el16-dl8,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-nh4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-ff12000,google,['arxiv.org/abs/2109.10686']
uer/chinese_roberta_L-12_H-512,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
tner/bert-base-tweetner7-continuous,tner,['bibtex']
Helsinki-NLP/opus-mt-tc-big-zle-es,Helsinki-NLP,['bibtex']
google/multiberts-seed_2-step_500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_40k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_120k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-base-nl40,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_3-step_1800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_1600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_1400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_160k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
coastalcph/fairlex-ecthr-minilm,coastalcph,['bibtex']
Helsinki-NLP/opus-mt-tc-base-uk-ces_slk,Helsinki-NLP,['bibtex']
keras-io/supervised-contrastive-learning-cifar10,keras-io,['arxiv.org/abs/2004.11362']
google/multiberts-seed_0-step_1200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_0-step_140k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Helsinki-NLP/opus-mt-tc-big-de-gmq,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-gmq-tr,Helsinki-NLP,['bibtex']
laihuiyuan/mFLAG,laihuiyuan,"['arxiv.org/abs/2209.01835', 'bibtex']"
l3cube-pune/marathi-bert-scratch,l3cube-pune,['arxiv.org/abs/2202.01159']
Helsinki-NLP/opus-mt-tc-big-he-gmq,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-gmq-gmq,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-gmq-zlw,Helsinki-NLP,['bibtex']
rufimelo/Legal-BERTimbau-sts-large-ma-v3,rufimelo,['bibtex']
Helsinki-NLP/opus-mt-tc-big-gmq-he,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-gmq-itc,Helsinki-NLP,['bibtex']
google/multiberts-seed_0-step_180k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
pcuenq/stable-diffusion-v1-4,pcuenq,['arxiv.org/abs/2207.12598']
facebook/regnet-y-10b-seer,facebook,['arxiv.org/abs/2003.13678']
IIC/dpr-spanish-passage_encoder-squades-base,IIC,['arxiv.org/abs/2004.04906']
tner/roberta-base-tweetner7-continuous,tner,['bibtex']
Helsinki-NLP/opus-mt-tc-big-itc-he,Helsinki-NLP,['bibtex']
google/multiberts-seed_3-step_400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
jfreiwa/asr-crdnn-german,jfreiwa,['bibtex']
google/multiberts-seed_0-step_1400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
north/t5_xl_NCC,north,['arxiv.org/abs/2104.09617']
sohomghosh/LIPI_FinSim3_Hypernym,sohomghosh,['bibtex']
Helsinki-NLP/opus-mt-tc-base-uk-ro,Helsinki-NLP,['bibtex']
google/multiberts-seed_1-step_80k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_20k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
lightonai/RITA_m,lightonai,"['arxiv.org/abs/2205.05789', {'title': 'RITA: a Study on Scaling Up Generative Protein Sequence Models'}]"
google/multiberts-seed_2-step_60k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-small-el16-dl2,google,['arxiv.org/abs/2109.10686']
ahsanjavid/convnext-tiny-finetuned-cifar10,ahsanjavid,['arxiv.org/abs/2201.03545']
google/multiberts-seed_2-step_800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_1800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
rbawden/CCASS-auto-titrages-base,rbawden,['bibtex']
google/multiberts-seed_4-step_1500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
manueltonneau/bert-twitter-pt-is-unemployed,manueltonneau,['arxiv.org/abs/2203.09178']
Geotrend/distilbert-base-25lang-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Multilingual BERT'}]
google/multiberts-seed_2-step_80k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Hate-speech-CNERG/marathi-codemixed-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
google/multiberts-seed_3-step_140k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_0k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_4-step_200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
bigscience/bloom-3b-intermediate,bigscience,['arxiv.org/abs/1909.08053']
Helsinki-NLP/opus-mt-tc-big-en-gmq,Helsinki-NLP,['bibtex']
google/multiberts-seed_2-step_160k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
w11wo/javanese-roberta-small-imdb-classifier,w11wo,"['arxiv.org/abs/1907.11692', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
facebook/regnet-y-002,facebook,['arxiv.org/abs/2003.13678']
facebook/wav2vec2-base-10k-voxpopuli-ft-nl,facebook,['arxiv.org/abs/2101.00390']
espnet/kan-bayashi_vctk_multi_spk_vits,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/multiberts-seed_4-step_700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_1000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
bigscience/bloom-1b1-intermediate,bigscience,['arxiv.org/abs/1909.08053']
google/multiberts-seed_1-step_700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
mrm8488/convbert-small-spanish,mrm8488,['arxiv.org/abs/2008.02496']
facebook/regnet-y-006,facebook,['arxiv.org/abs/2003.13678']
facebook/wav2vec2-base-10k-voxpopuli-ft-sl,facebook,['arxiv.org/abs/2101.00390']
w11wo/wav2vec2-xls-r-300m-korean,w11wo,['arxiv.org/abs/2111.09296']
ccdv/lsg-bert-base-uncased-4096,ccdv,"['arxiv.org/abs/1810.04805', 'bibtex']"
relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce,relbert,['bibtex']
Helsinki-NLP/opus-mt-tc-big-ar-gmq,Helsinki-NLP,['bibtex']
tner/roberta-base-tweetner7-all,tner,['bibtex']
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
north/t5_xxl_NCC_lm,north,['arxiv.org/abs/2104.09617']
xfbai/AMRBART-base,xfbai,['bibtex']
facebook/regnet-y-640-seer-in1k,facebook,['arxiv.org/abs/2202.08360']
hackathon-pln-es/unam_tesis_BETO_finnetuning,hackathon-pln-es,"[{'title': ""UNAM's Theses with BETO fine-tuning classify""}]"
UBC-NLP/prags1,UBC-NLP,['bibtex']
ZedTheUndead/bloom_test,ZedTheUndead,['arxiv.org/abs/1909.08053']
Loc/lucky-model,Loc,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
google/multiberts-seed_2-step_1100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_1700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_1200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
allenai/mtk-instruct-11b-def-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
google/t5-efficient-large-el2,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-nh32,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-nl40,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-large-dm2000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-el12,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_2-step_40k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-small-el16-dl4,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-el2,google,['arxiv.org/abs/2109.10686']
google/multiberts-seed_0-step_400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/t5-efficient-large-nh8-nl32,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-base-dm1000,google,['arxiv.org/abs/2109.10686']
AbhirupGhosh/opus-mt-finetuned-en-hi,AbhirupGhosh,['arxiv.org/abs/1706.03762']
google/multiberts-seed_2-step_1000k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
tner/twitter-roberta-base-2019-90m-tweetner7-2020,tner,['bibtex']
north/t5_xl_NCC_lm,north,['arxiv.org/abs/2104.09617']
tner/roberta-base-tweetner7-2020,tner,['bibtex']
tner/roberta-base-tweetner7-2021,tner,['bibtex']
google/multiberts-seed_3-step_180k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_1700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Matthijs/mobilenet_v1_1.0_224,Matthijs,['arxiv.org/abs/1704.04861']
EventMiner/xlm-roberta-large-en-pt-es-doc,EventMiner,['bibtex']
facebook/wav2vec2-conformer-rope-large-100h-ft,facebook,['arxiv.org/abs/2010.05171']
google/multiberts-seed_3,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
north/t5_xxl_NCC,north,['arxiv.org/abs/2104.09617']
xlm-mlm-enro-1024,huggingface,"['arxiv.org/abs/1901.07291', {'title': 'Cross-lingual language model pretraining'}]"
it5/it5-base-informal-to-formal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
google/multiberts-seed_4-step_160k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
espnet/kan-bayashi_csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
manueltonneau/bert-twitter-en-is-unemployed,manueltonneau,['arxiv.org/abs/2203.09178']
google/multiberts-seed_2-step_1700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_20k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_4-step_900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
tner/twitter-roberta-base-2019-90m-tweetner7-2021,tner,['bibtex']
slone/LaBSE-en-ru-myv-v1,slone,['arxiv.org/abs/2209.09368']
google/multiberts-seed_1-step_60k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
MoseliMotsoehli/TswanaBert,MoseliMotsoehli,"['doi.org/10.5281/zenodo.3668495', 'bibtex']"
espnet/kan-bayashi_vctk_gst_fastspeech,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
allenai/tk-instruct-11b-def-pos-neg-expl,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
google/multiberts-seed_1-step_800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
espnet/kan-bayashi_csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/multiberts-seed_4-step_1700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
ufal/byt5-small-multilexnorm2021-iden,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
google/multiberts-seed_4-step_1400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_1500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_1300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_1500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
espnet/kan-bayashi_jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with-truncated-ba3566,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
sismetanin/xlm_roberta_large-ru-sentiment-rusentiment,sismetanin,['bibtex']
facebook/vit-msn-large,facebook,"['arxiv.org/abs/2204.07141', {'title': 'Masked Siamese Networks for Label-Efficient Learning'}]"
facebook/regnet-y-004,facebook,['arxiv.org/abs/2003.13678']
google/multiberts-seed_2-step_1600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
sismetanin/rubert_conversational-ru-sentiment-rusentiment,sismetanin,['bibtex']
google/multiberts-seed_3-step_600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_1200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_4-step_800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
fgaim/tiroberta-sentiment,fgaim,['bibtex']
alibaba-pai/pai-bert-base-zh,alibaba-pai,"['arxiv.org/abs/2205.00258', {'title': 'EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing'}]"
fgaim/tiroberta-geezswitch,fgaim,[{'title': 'GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages'}]
allenai/tk-instruct-small-def-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
Davlan/afro-xlmr-mini,Davlan,['arxiv.org/abs/2204.06487']
google/multiberts-seed_4-step_1100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_4-step_1200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_1800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/regnet-x-006,facebook,['arxiv.org/abs/2003.13678']
facebook/regnet-y-120,facebook,['arxiv.org/abs/2003.13678']
espnet/kan-bayashi_csmsc_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
gchhablani/bert-base-cased-finetuned-stsb,gchhablani,['arxiv.org/abs/2105.03824']
google/multiberts-seed_1-step_1200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
hfl/chinese-electra-small-ex-discriminator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
Helsinki-NLP/opus-mt-tc-base-gmw-gmw,Helsinki-NLP,['bibtex']
it5/mt5-base-formal-to-informal,it5,"['arxiv.org/abs/2203.03759', {'title': 'IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation'}]"
facebook/wav2vec2-large-es-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
google/multiberts-seed_4-step_180k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
ychenNLP/arabic-relation-extraction,ychenNLP,['bibtex']
pyf98/aishell_branchformer_e24_amp,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/regnet-x-080,facebook,['arxiv.org/abs/2003.13678']
m3hrdadfi/bert-fa-base-uncased-farstail,m3hrdadfi,[{'title': 'FarsTail: A Persian Natural Language Inference Dataset'}]
jhu-clsp/roberta-large-eng-ara-128k,jhu-clsp,['bibtex']
google/multiberts-seed_1-step_1500k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_1900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
ccdv/lsg-bart-base-16384,ccdv,"['arxiv.org/abs/1910.13461', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
google/multiberts-seed_4-step_1800k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
BSC-TeMU/roberta-base-ca,BSC-TeMU,"['doi.org/10.5281/zenodo.4762030)', 'bibtex']"
google/multiberts-seed_1-step_100k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
espnet/vectominist_seame_asr_conformer_bpe5626,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/multiberts-seed_3-step_120k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
aliosm/ComVE-gpt2,aliosm,[{'title': 'JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation'}]
google/multiberts-seed_2-step_600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
ydshieh/clip-vit-base-patch32,ydshieh,['arxiv.org/abs/2007.14062']
manueltonneau/bert-twitter-pt-lost-job,manueltonneau,['arxiv.org/abs/2203.09178']
airesearch/wangchanberta-base-wiki-sefr,airesearch,"['arxiv.org/abs/1907.11692', 'doi.org/10.5281/zenodo.3457707).']"
google/multiberts-seed_4-step_400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_2-step_1300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
indobenchmark/indogpt,indobenchmark,"['arxiv.org/abs/2104.08200', {'title': 'IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation'}]"
google/multiberts-seed_1-step_160k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/regnet-y-032,facebook,['arxiv.org/abs/2003.13678']
Matthijs/mobilenet_v2_1.0_224,Matthijs,"['arxiv.org/abs/1801.04381', {'title': 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'}]"
google/multiberts-seed_2-step_1400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
manueltonneau/bert-twitter-en-lost-job,manueltonneau,['arxiv.org/abs/2203.09178']
nvidia/nemo-megatron-mt5-3B,nvidia,['arxiv.org/abs/2010.11934']
bigscience/bloom-176-intermediate,bigscience,['arxiv.org/abs/1909.08053']
microsoft/unispeech-large-multi-lingual-1500h-cv,microsoft,['arxiv.org/abs/2101.07597']
mrm8488/spanbert-base-finetuned-tacred,mrm8488,['arxiv.org/abs/1907.10529']
google/multiberts-seed_3-step_1900k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
tner/bertweet-large-tweetner7-all,tner,['bibtex']
doc2query/msmarco-arabic-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
lightonai/RITA_xl,lightonai,"['arxiv.org/abs/2205.05789', {'title': 'RITA: a Study on Scaling Up Generative Protein Sequence Models'}]"
google/multiberts-seed_7,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
RUCAIBox/mvp-task-dialog,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
google/multiberts-seed_2-step_140k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_1400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/regnet-x-064,facebook,['arxiv.org/abs/2003.13678']
mukayese/mbart-large-turkish-summarization,mukayese,['arxiv.org/abs/2203.01215']
xfbai/AMRBART-large-finetuned-AMR2.0-AMR2Text,xfbai,['bibtex']
facebook/regnet-x-320,facebook,['arxiv.org/abs/2003.13678']
imvladikon/general_character_bert,imvladikon,['bibtex']
Helsinki-NLP/opus-mt-tc-big-itc-ar,Helsinki-NLP,['bibtex']
saahith/wav2vec2_base_100h_test,saahith,['arxiv.org/abs/2006.11477']
princeton-nlp/efficient_mlm_m0.80,princeton-nlp,['arxiv.org/abs/2202.08005']
facebook/regnet-x-016,facebook,['arxiv.org/abs/2003.13678']
laituan245/molt5-base-smiles2caption,laituan245,['arxiv.org/abs/2204.11817']
princeton-nlp/efficient_mlm_m0.40-801010,princeton-nlp,['arxiv.org/abs/2202.08005']
princeton-nlp/efficient_mlm_m0.60,princeton-nlp,['arxiv.org/abs/2202.08005']
sismetanin/xlm_roberta_base-ru-sentiment-rusentiment,sismetanin,['bibtex']
l3cube-pune/hing-gpt,l3cube-pune,['arxiv.org/abs/2204.08398']
UWB-AIR/Czert-A-base-uncased,UWB-AIR,"['arxiv.org/abs/2103.13031', {'title': 'Czert -- Czech BERT-like Model for Language Representation'}]"
google/multiberts-seed_4-step_300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
RUCAIBox/mtl-question-generation,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
mlcorelib/debertav2-base-uncased,mlcorelib,"['arxiv.org/abs/1810.04805', 'bibtex']"
facebook/regnet-x-120,facebook,['arxiv.org/abs/2003.13678']
Hate-speech-CNERG/bengali-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
asapp/sew-d-base-100k,asapp,['arxiv.org/abs/2109.06870']
facebook/xm_transformer_600m-en_fr-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
Geotrend/distilbert-base-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
GroNLP/bert-base-dutch-cased-upos-alpino,GroNLP,['arxiv.org/abs/2105.02855']
w11wo/lao-roberta-base-pos-tagger,w11wo,['arxiv.org/abs/1907.11692']
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
google/multiberts-seed_1-step_200k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
manueltonneau/bert-twitter-pt-is-hired,manueltonneau,['arxiv.org/abs/2203.09178']
baffo32/pyc2py_alpha2,baffo32,['arxiv.org/abs/1907.06292']
google/multiberts-seed_1-step_300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Rachneet/t5-base-qg-hl-squadv2,Rachneet,['arxiv.org/abs/1910.10683']
espnet/Yushi_Ueda_ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256-truncated-eb42e5,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
uer/chinese_roberta_L-6_H-512,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
MoseliMotsoehli/zuBERTa,MoseliMotsoehli,['bibtex']
google/multiberts-seed_4-step_600k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_1-step_400k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_3-step_700k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/m2m100-12B-avg-10-ckpt,facebook,['arxiv.org/abs/2010.11125']
google/multiberts-seed_3-step_1300k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/wav2vec2-base-en-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
saurkulsh/T0pp,saurkulsh,['arxiv.org/abs/2110.08207']
cardiffnlp/roberta-base-tweet-topic-multi-all,cardiffnlp,['bibtex']
princeton-nlp/efficient_mlm_m0.20,princeton-nlp,['arxiv.org/abs/2202.08005']
google/tapas-tiny-finetuned-sqa,google,['arxiv.org/abs/2004.02349']
it5/mt5-base-informal-to-formal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
princeton-nlp/efficient_mlm_m0.50,princeton-nlp,['arxiv.org/abs/2202.08005']
princeton-nlp/efficient_mlm_m0.70,princeton-nlp,['arxiv.org/abs/2202.08005']
Helsinki-NLP/opus-mt-tc-big-zls-de,Helsinki-NLP,['bibtex']
espnet/kan-bayashi_ljspeech_fastspeech,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
princeton-nlp/efficient_mlm_m0.15-801010,princeton-nlp,['arxiv.org/abs/2202.08005']
flax-sentence-embeddings/multi-qa_v1-mpnet-cls_dot,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
princeton-nlp/efficient_mlm_m0.40,princeton-nlp,['arxiv.org/abs/2202.08005']
princeton-nlp/efficient_mlm_m0.30,princeton-nlp,['arxiv.org/abs/2202.08005']
facebook/wav2vec2-large-uralic-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
facebook/regnet-x-008,facebook,['arxiv.org/abs/2003.13678']
rufimelo/Legal-BERTimbau-sts-base-ma,rufimelo,['bibtex']
tau/t5-v1_1-large-rss,tau,"['arxiv.org/abs/2108.05857', 'bibtex']"
sismetanin/rubert-ru-sentiment-rureviews,sismetanin,['bibtex']
tsantosh7/Bailii-Roberta,tsantosh7,['arxiv.org/abs/1907.11692']
canwenxu/laprador,canwenxu,['arxiv.org/abs/2203.06169']
Finnish-NLP/roberta-large-wechsel-finnish,Finnish-NLP,['arxiv.org/abs/1907.11692']
facebook/regnet-x-160,facebook,['arxiv.org/abs/2003.13678']
princeton-nlp/efficient_mlm_m0.15,princeton-nlp,['arxiv.org/abs/2202.08005']
espnet/kan-bayashi_libritts_tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no-truncated-09d645,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
dbmdz/bert-base-historic-multilingual-64k-td-cased,dbmdz,['arxiv.org/abs/2205.15575']
espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
laituan245/molt5-base-caption2smiles,laituan245,['arxiv.org/abs/2204.11817']
google/multiberts-seed_24,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
facebook/regnet-x-032,facebook,['arxiv.org/abs/2003.13678']
morenolq/thext-bio-scibert,morenolq,"['doi.org/10.1016/j.knosys.2022.109382).', {'title': 'Transformer-based highlights extraction from scientific papers'}]"
Hate-speech-CNERG/tamil-codemixed-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
espnet/kan-bayashi_ljspeech_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/regnet-y-080,facebook,['arxiv.org/abs/2003.13678']
facebook/regnet-y-064,facebook,['arxiv.org/abs/2003.13678']
3ebdola/Dialectal-Arabic-XLM-R-Base,3ebdola,"['doi.org/10.1016/j.ipm.2022.102964},', {'title': 'AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling'}]"
IIC/roberta-base-bne-bioasq,IIC,['arxiv.org/abs/2107.07253']
Helsinki-NLP/opus-mt-tc-big-zle-itc,Helsinki-NLP,['bibtex']
manueltonneau/bert-twitter-es-lost-job,manueltonneau,['arxiv.org/abs/2203.09178']
facebook/wav2vec2-base-10k-voxpopuli-ft-hu,facebook,['arxiv.org/abs/2101.00390']
facebook/xm_transformer_600m-en_tr-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
google/multiberts-seed_1-step_120k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
uer/chinese_roberta_L-6_H-256,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
google/multiberts-seed_1-step_140k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
espnet/french_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
schhwmn/mbart-large-50-finetuned-ukr-gec,schhwmn,['arxiv.org/abs/2103.16997']
facebook/wav2vec2-base-10k-voxpopuli-ft-it,facebook,['arxiv.org/abs/2101.00390']
google/ddpm-ema-celebahq-256,google,['arxiv.org/abs/2006.11239']
AdapterHub/bert-base-uncased-pf-quail,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
google/multiberts-seed_2-step_120k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
nglaura/skimformer,nglaura,"['arxiv.org/abs/2109.01078', {'title': 'Skim-Attention: Learning to Focus via Document Layout'}]"
espnet/kan_bayashi_jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/regnet-x-004,facebook,['arxiv.org/abs/2003.13678']
Yaxin/ernie_1.0_skep_large_ch,Yaxin,[{'title': 'SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis'}]
google/multiberts-seed_2-step_180k,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
flax-community/wav2vec2-spanish,flax-community,['arxiv.org/abs/2006.11477']
akdeniz27/convbert-base-turkish-cased-ner,akdeniz27,['arxiv.org/abs/2008.02496']
smartmind/roberta-ko-small-tsdae,smartmind,['arxiv.org/abs/2104.06979']
nielsr/beit-base-patch16-224,nielsr,['arxiv.org/abs/2106.08254']
tner/bertweet-base-tweetner7-random,tner,['bibtex']
w11wo/sundanese-gpt2-base-emotion-classifier,w11wo,"['doi.org/10.21203/rs.3.rs-907893/v1}', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-en-bg,Helsinki-NLP,['bibtex']
google/ncsnpp-celebahq-256,google,['arxiv.org/abs/2011.13456']
facebook/vit-msn-large-7,facebook,"['arxiv.org/abs/2204.07141', {'title': 'Masked Siamese Networks for Label-Efficient Learning'}]"
tner/bert-base-tweetner7-2021,tner,['bibtex']
RamAnanth1/decision_transformers_half_cheetah,RamAnanth1,['arxiv.org/abs/2106.01345']
Helsinki-NLP/opus-mt-tc-big-he-itc,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-gmq-en,Helsinki-NLP,['bibtex']
efederici/convnext-base-224-22k-1k-orig-cats-vs-dogs,efederici,"['arxiv.org/abs/2201.03545', 'bibtex']"
Sampson2022/test2,Sampson2022,"['arxiv.org/abs/1512.03385', {'title': 'Deep residual learning for image recognition'}]"
espnet/kan-bayashi_jsut_tacotron2_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
kenobi/SDO_VT1,kenobi,[{'title': 'Imagenet: A large-scale hierarchical image database'}]
fusing/latent-diffusion-text2im-large,fusing,['arxiv.org/abs/2112.10752']
mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof,mbeukman,['arxiv.org/abs/2103.11811']
izumi-lab/electra-small-japanese-generator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
espnet/kan-bayashi_vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g-truncated-50b003,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-itc-itc,Helsinki-NLP,['bibtex']
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/regnet-y-008,facebook,['arxiv.org/abs/2003.13678']
Visual-Attention-Network/van-large,Visual-Attention-Network,['arxiv.org/abs/2202.09741']
mrm8488/bloom-1b3-8bit,mrm8488,['arxiv.org/abs/2106.09685']
facebook/regnet-y-016,facebook,['arxiv.org/abs/2003.13678']
facebook/regnet-y-160,facebook,['arxiv.org/abs/2003.13678']
espnet/GunnarThor_talromur_h_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
EleutherAI/enformer-preview,EleutherAI,['doi.org/10.1038/s41592-021-01252-x']
flax-sentence-embeddings/all_datasets_v3_MiniLM-L12,flax-sentence-embeddings,"['arxiv.org/abs/1810.09305', 'doi.org/10.18653/v1/d15-1075),']"
flax-community/gpt-neo-1.3B-apps-all,flax-community,['arxiv.org/abs/2107.03374']
google/t5-efficient-small-dl12,google,['arxiv.org/abs/2109.10686']
wanyu/IteraTeR-BART-Revision-Generator,wanyu,['arxiv.org/abs/2203.03802']
espnet/siddhana_slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-cola,AdapterHub,['bibtex']
aliosm/ComVE-gpt2-large,aliosm,[{'title': 'JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation'}]
zlucia/bert-double,zlucia,"['arxiv.org/abs/2104.08671', {'title': 'When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset'}]"
Geotrend/distilbert-base-pl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Matthijs/mobilenet_v2_1.4_224,Matthijs,"['arxiv.org/abs/1801.04381', {'title': 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'}]"
google/tapas-medium-finetuned-tabfact,google,"['arxiv.org/abs/2010.00571', {'title': 'TabFact : A Large-scale Dataset for Table-based Fact Verification'}]"
espnet/kan-bayashi_vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_-truncated-69081b,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
doc2query/msmarco-spanish-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
AnReu/math_pretrained_roberta,AnReu,[{'title': 'Transformer-Encoder and Decoder Models for Questions on Math'}]
microsoft/xclip-base-patch16-16-frames,microsoft,['arxiv.org/abs/2208.02816']
AnReu/albert-for-arqmath-3,AnReu,[{'title': 'Transformer-Encoder and Decoder Models for Questions on Math'}]
uer/chinese_roberta_L-10_H-512,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
asapp/sew-mid-100k,asapp,['arxiv.org/abs/2109.06870']
facebook/regnet-y-320,facebook,['arxiv.org/abs/2003.13678']
AdapterHub/roberta-base-pf-fce_error_detection,AdapterHub,['bibtex']
flax-sentence-embeddings/all_datasets_v4_MiniLM-L12,flax-sentence-embeddings,"['arxiv.org/abs/1810.09305', 'doi.org/10.18653/v1/d15-1075),']"
NimaBoscarino/efficientformer-l1-1000,NimaBoscarino,"['arxiv.org/abs/2206.01191', {'title': 'EfficientFormer: Vision Transformers at MobileNet Speed'}]"
facebook/regnet-x-002,facebook,['arxiv.org/abs/2003.13678']
izumi-lab/electra-small-paper-japanese-discriminator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
pyf98/librispeech_conformer_hop_length160,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
sail/poolformer_m36,sail,"['arxiv.org/abs/2111.11418', {'title': 'MetaFormer is Actually What You Need for Vision'}]"
espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Splend1dchan/wav2vec2-large-10min-lv60-self,Splend1dchan,['arxiv.org/abs/2010.11430']
m3hrdadfi/albert-fa-base-v2-ner-peyma,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
manueltonneau/bert-twitter-es-is-unemployed,manueltonneau,['arxiv.org/abs/2203.09178']
kinit/slovakbert-sts-stsb,kinit,"['arxiv.org/abs/2109.15254', 'bibtex']"
google/multiberts-seed_13,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
digio/Twitter4SSE,digio,"['arxiv.org/abs/2110.02030', 'bibtex']"
manueltonneau/bert-twitter-es-job-search,manueltonneau,['arxiv.org/abs/2203.09178']
uer/chinese_roberta_L-12_H-256,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
IIC/beto-base-cased-bioasq,IIC,['arxiv.org/abs/2107.07253']
manueltonneau/bert-twitter-es-job-offer,manueltonneau,['arxiv.org/abs/2203.09178']
allenai/wmt16-en-de-dist-12-1,allenai,['arxiv.org/abs/2006.10369']
flax-sentence-embeddings/multi-qa_v1-distilbert-cls_dot,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
espnet/chai_librispeech_asr_train_rnnt_conformer_raw_en_bpe5000_sp,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/wav2vec2-large-sv-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
nvidia/groupvit-gcc-redcaps,nvidia,"['arxiv.org/abs/2202.11094', 'bibtex']"
manueltonneau/bert-twitter-es-is-hired,manueltonneau,['arxiv.org/abs/2203.09178']
flax-sentence-embeddings/multi-qa_v1-distilbert-mean_cos,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
it5/mt5-base-news-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
tner/roberta-base-tweetner7-random,tner,['bibtex']
tner/twitter-roberta-base-dec2020-tweetner7-2020,tner,['bibtex']
Monsia/afrilang-bci-tts,Monsia,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
eugenesiow/rcan-bam,eugenesiow,['arxiv.org/abs/1807.02758']
tals/albert-base-vitaminc-mnli,tals,['bibtex']
fgaim/tiroberta-pos,fgaim,['bibtex']
AdapterHub/bert-base-uncased-pf-qqp,AdapterHub,['bibtex']
facebook/regnet-y-640-seer,facebook,['arxiv.org/abs/2202.08360']
facebook/xm_transformer_600m-en_ar-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
google/multiberts-seed_4,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
AdapterHub/bert-base-uncased-pf-wikihop,AdapterHub,['bibtex']
google/multiberts-seed_17,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
jaketae/hifigan-lj-v1,jaketae,['arxiv.org/abs/2010.05646']
OWG/bert-base-uncased,OWG,['arxiv.org/abs/1810.04805']
google/multiberts-seed_1,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
p208p2002/gpt2-squad-nqg-hl,p208p2002,['arxiv.org/abs/1606.05250']
aapot/wav2vec2-xlsr-1b-finnish,aapot,['arxiv.org/abs/2111.09296']
w11wo/javanese-gpt2-small-imdb,w11wo,[{'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]
ccdv/lsg-bart-large-4096,ccdv,"['arxiv.org/abs/1910.13461', 'bibtex']"
google/multiberts-seed_2,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
testorg2/larger_fork,testorg2,"['arxiv.org/abs/1908.10084', 'bibtex']"
espnet/kan-bayashi_jvs_jvs010_vits_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_-truncated-ec9e34,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-de-es,Helsinki-NLP,['bibtex']
tner/bert-large-tweetner7-2020,tner,['bibtex']
wf-genius/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese,wf-genius,"['arxiv.org/abs/2209.02970', 'bibtex']"
tner/twitter-roberta-base-dec2020-tweetner7-random,tner,['bibtex']
tner/deberta-v3-large-mit-movie-trivia,tner,['bibtex']
espnet/Emiru_Tsunoo_aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
coastalcph/fairlex-cail-minilm,coastalcph,['bibtex']
google/multiberts-seed_16,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
it5/it5-small-informal-to-formal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
ccdv/lsg-legal-small-uncased-4096,ccdv,['bibtex']
m3hrdadfi/albert-fa-base-v2-sentiment-digikala,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-igbo,mbeukman,['arxiv.org/abs/2103.11811']
benjamin/gpt2-wechsel-ukrainian,benjamin,['arxiv.org/abs/2112.06598']
ccdv/lsg-legal-base-uncased-4096,ccdv,['bibtex']
google/multiberts-seed_23,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
google/multiberts-seed_20,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
m3hrdadfi/albert-fa-base-v2-sentiment-snappfood,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
w11wo/javanese-gpt2-small-imdb-classifier,w11wo,[{'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]
flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-mean_cos,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
Intel/bert-large-uncased-squadv1.1-sparse-90-unstructured,Intel,['arxiv.org/abs/2111.05754']
MultiBertGunjanPatrick/multiberts-seed-4-1100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
classla/sloberta-frenk-hate,classla,"['arxiv.org/abs/1907.11692', 'bibtex']"
google/multiberts-seed_9,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
danjohnvelasco/filipino-sentence-roberta-v1,danjohnvelasco,"['arxiv.org/abs/2204.03251', 'doi.org/10.48550/arxiv.2204.03251,']"
Helsinki-NLP/opus-mt-tc-big-zls-en,Helsinki-NLP,['bibtex']
yhavinga/t5-base-36L-dutch-english-cased,yhavinga,['arxiv.org/abs/2109.10686']
yelpfeast/byt5-base-english-ocr-correction,yelpfeast,['arxiv.org/abs/2105.13626']
lgris/bp500-xlsr,lgris,['arxiv.org/abs/2012.03411']
Geotrend/bert-base-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-en-da-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
yhavinga/t5-v1.1-large-dutch-cased,yhavinga,['arxiv.org/abs/2109.10686']
benjamin/roberta-base-wechsel-chinese,benjamin,['bibtex']
ChainYo/segformer-b1-sidewalk,ChainYo,['arxiv.org/abs/2105.15203']
keithhon/paraphrase-multilingual-MiniLM-L12-v2,keithhon,"['arxiv.org/abs/1908.10084', 'bibtex']"
Helsinki-NLP/opus-mt-tc-base-ro-uk,Helsinki-NLP,['bibtex']
facebook/wav2vec2-large-fr-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
keras-io/cct,keras-io,['arxiv.org/abs/2010.11929']
rohitsroch/hybrid_utt-clusterrank_bart-base_samsum_sum,rohitsroch,"['doi.org/10.1145/3508546.3508640*', 'bibtex']"
facebook/roberta-hate-speech-dynabench-r1-target,facebook,"['arxiv.org/abs/2012.15761', {'title': 'Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection'}]"
baffo32/genji-python-6B-split,baffo32,['arxiv.org/abs/2104.09864']
facebook/wav2vec2-base-10k-voxpopuli-ft-hr,facebook,['arxiv.org/abs/2101.00390']
w11wo/sundanese-roberta-base-emotion-classifier,w11wo,"['arxiv.org/abs/1907.11692', 'doi.org/10.21203/rs.3.rs-907893/v1}', 'bibtex']"
microsoft/unispeech-1350-en-168-es-ft-1h,microsoft,['arxiv.org/abs/2101.07597']
benjamin/gpt2-wechsel-german,benjamin,['bibtex']
edbeeching/decision-transformer-gym-hopper-medium-replay,edbeeching,['arxiv.org/abs/2106.01345']
espnet/tamil_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/regnet-y-10b-seer-in1k,facebook,['arxiv.org/abs/2003.13678']
AnReu/math_pretrained_bert,AnReu,[{'title': 'Transformer-Encoder and Decoder Models for Questions on Math'}]
TweebankNLP/bertweet-tb2_wnut17-ner,TweebankNLP,[{'title': 'Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis'}]
LeBenchmark/wav2vec2-FR-2.6K-base,LeBenchmark,[{'title': 'LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech'}]
google/multiberts-seed_21,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Geotrend/distilbert-base-en-fr-zh-ja-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
RUCAIBox/mvp-question-answering,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
eugenesiow/pan-bam,eugenesiow,['arxiv.org/abs/2010.01073']
flax-sentence-embeddings/all_datasets_v3_MiniLM-L6,flax-sentence-embeddings,"['arxiv.org/abs/1810.09305', 'doi.org/10.18653/v1/d15-1075),']"
yhavinga/t5-v1.1-base-dutch-uncased,yhavinga,['arxiv.org/abs/2109.10686']
AdapterHub/bert-base-uncased-pf-mnli,AdapterHub,['bibtex']
AdapterHub/bert-base-uncased-pf-race,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
espnet/kan-bayashi_jsut_fastspeech2_accent,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/xm_transformer_600m-en_zh-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
course5i/SEAD-L-6_H-384_A-12-mnli,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
tner/bertweet-base-tweetner7-2021,tner,['bibtex']
CK42/sentiment_analysis_sbcBI,CK42,['arxiv.org/abs/1810.04805']
espnet/jiyangtang_magicdata_asr_conformer_lm_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-hotpotqa,AdapterHub,['bibtex']
yanaiela/roberta-base-epoch_76,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
facebook/wav2vec2-base-10k-voxpopuli-ft-ro,facebook,['arxiv.org/abs/2101.00390']
lgris/bp400-xlsr,lgris,['arxiv.org/abs/2107.11414']
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
valurank/headline_similarities,valurank,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
rsuwaileh/IDRISI-LMR-EN-timebased-typebased,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
IIC/dpr-spanish-question_encoder-squades-base,IIC,['arxiv.org/abs/2004.04906']
edbeeching/decision-transformer-gym-halfcheetah-medium-replay,edbeeching,['arxiv.org/abs/2106.01345']
speechbrain/slu-direct-fluent-speech-commands-librispeech-asr,speechbrain,['bibtex']
mideind/IceBERT-igc,mideind,"['arxiv.org/abs/2201.05601', 'bibtex']"
espnet/kan-bayashi_vctk_gst_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
fusing/ddim-celeba-hq,fusing,['arxiv.org/abs/2010.02502']
facebook/data2vec-audio-large-100h,facebook,['arxiv.org/abs/2202.03555']
facebook/s2t-small-mustc-en-es-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
gaetangate/bart-large_genrl_lcquad2,gaetangate,"['arxiv.org/abs/2108.07337', {'title': 'Generative relation linking for question answering over knowledge bases'}]"
relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce,relbert,['bibtex']
google/t5-efficient-small-ff1000,google,['arxiv.org/abs/2109.10686']
madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1,madlag,['arxiv.org/abs/2005.07683']
facebook/roberta-hate-speech-dynabench-r2-target,facebook,"['arxiv.org/abs/2012.15761', {'title': 'Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection'}]"
rufimelo/Legal-BERTimbau-sts-large-ma,rufimelo,['bibtex']
RUCAIBox/mvp-question-generation,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
espnet/kan-bayashi_vctk_tts_train_gst_xvector_conformer_fastspeech2_transform-truncated-e051a9,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
w11wo/sundanese-bert-base-emotion-classifier,w11wo,"['arxiv.org/abs/1810.04805', 'doi.org/10.21203/rs.3.rs-907893/v1}', 'bibtex']"
facebook/wav2vec2-base-10k-voxpopuli-ft-sk,facebook,['arxiv.org/abs/2101.00390']
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-hausa,mbeukman,['arxiv.org/abs/2103.11811']
yhavinga/t5-base-dutch,yhavinga,['arxiv.org/abs/2109.10686']
mukayese/mt5-base-turkish-summarization,mukayese,['arxiv.org/abs/2203.01215']
LeBenchmark/wav2vec2-FR-1K-base,LeBenchmark,[{'title': 'LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech'}]
google/multiberts-seed_6,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Helsinki-NLP/opus-mt-tc-big-gmw-gmw,Helsinki-NLP,['bibtex']
google/tapas-medium-finetuned-sqa,google,['arxiv.org/abs/2004.02349']
SaulLu/recreate-history,SaulLu,['bibtex']
Finnish-NLP/t5-large-nl36-finnish,Finnish-NLP,['arxiv.org/abs/1910.10683']
benjamin/gpt2-wechsel-uyghur,benjamin,['arxiv.org/abs/2112.06598']
nvidia/stt_zh_citrinet_1024_gamma_0_25,nvidia,['arxiv.org/abs/2104.01721']
gchhablani/bert-base-cased-finetuned-wnli,gchhablani,['arxiv.org/abs/2105.03824']
chrisliu298/arxiv_ai_gpt2,chrisliu298,['doi.org/10.5281/zenodo.2533436}']
spacy/ca_core_news_lg,spacy,['doi.org/10.5281/zenodo.4522041)']
AdapterHub/roberta-base-pf-sst2,AdapterHub,['bibtex']
superb/hubert-large-superb-sid,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
uer/chinese_roberta_L-10_H-256,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
google/t5-efficient-tiny-el6,google,['arxiv.org/abs/2109.10686']
sramasamy8/testModel,sramasamy8,"['arxiv.org/abs/1810.04805', 'bibtex']"
morenolq/thext-ai-scibert,morenolq,"['doi.org/10.1016/j.knosys.2022.109382).', {'title': 'Transformer-based highlights extraction from scientific papers'}]"
tner/bert-base-tweetner7-all,tner,['bibtex']
morenolq/thext-cs-scibert,morenolq,"['doi.org/10.1016/j.knosys.2022.109382).', {'title': 'Transformer-based highlights extraction from scientific papers'}]"
allenai/wmt16-en-de-dist-6-1,allenai,['arxiv.org/abs/2006.10369']
espnet/kan-bayashi_libritts_xvector_vits,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/ddpm-cat-256,google,['arxiv.org/abs/2006.11239']
Helsinki-NLP/opus-mt-tc-big-lv-en,Helsinki-NLP,['bibtex']
AdapterHub/roberta-base-pf-conll2003,AdapterHub,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-2-400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
lichenda/wsj0_2mix_skim_noncausal,lichenda,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/vit-msn-base,facebook,"['arxiv.org/abs/2204.07141', {'title': 'Masked Siamese Networks for Label-Efficient Learning'}]"
google/t5-efficient-small-dm768,google,['arxiv.org/abs/2109.10686']
w11wo/wav2vec2-xls-r-300m-korean-lm,w11wo,['arxiv.org/abs/2111.09296']
chrisjay/fonxlsr,chrisjay,['arxiv.org/abs/2103.07762']
BSC-TeMU/roberta-base-bne-capitel-ner-plus,BSC-TeMU,['arxiv.org/abs/1907.11692']
espnet/kan-bayashi_csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-qnli,AdapterHub,['bibtex']
microsoft/unispeech-sat-large-sd,microsoft,['arxiv.org/abs/1912.07875']
spacy/ca_core_news_md,spacy,['doi.org/10.5281/zenodo.4522041)']
joaogante/test_audio,joaogante,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
ELiRF/mt5-base-dacsa-ca,ELiRF,"['arxiv.org/abs/2010.11934', 'bibtex']"
google/multiberts-seed_15,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
ctoraman/RoBERTa-TR-medium-wp-44k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
Helsinki-NLP/opus-mt-tc-big-zlw-en,Helsinki-NLP,['bibtex']
google/ncsnpp-church-256,google,['arxiv.org/abs/2011.13456']
rohanrajpal/bert-base-en-es-codemix-cased,rohanrajpal,['bibtex']
bigscience-data/sgpt-bloom-1b7-nli,bigscience-data,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
MultiBertGunjanPatrick/multiberts-seed-1-900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
aioxlabs/hifigan-swahili,aioxlabs,['arxiv.org/abs/2010.05646']
TweebankNLP/bertweet-tb2-ner,TweebankNLP,[{'title': 'Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis'}]
facebook/wav2vec2-base-fr-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
tner/bertweet-base-tweetner7-2020,tner,['bibtex']
qarib/bert-base-qarib60_1790k,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
google/tapas-mini-finetuned-sqa,google,['arxiv.org/abs/2004.02349']
cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-all,cardiffnlp,['bibtex']
brema76/vaccine_topic_it,brema76,"['arxiv.org/abs/2207.12264', {'title': 'Dynamics of information flow and engaging power of narratives in the polarised debate on vaccines'}]"
microsoft/xclip-base-patch16-hmdb-16-shot,microsoft,['arxiv.org/abs/2208.02816']
aioxlabs/tacotron-wolof,aioxlabs,['arxiv.org/abs/1712.05884']
espnet/kan-bayashi_vctk_gst_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
gilparmentier/pokemon_gptj_model,gilparmentier,['arxiv.org/abs/2104.09864']
dbmdz/electra-base-turkish-mc4-cased-discriminator,dbmdz,['doi.org/10.5281/zenodo.3770924}']
google/multiberts-seed_22,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
jfreiwa/asr-crdnn-german-umlaute,jfreiwa,['bibtex']
microsoft/xclip-base-patch16-hmdb-8-shot,microsoft,['arxiv.org/abs/2208.02816']
AdapterHub/roberta-base-pf-stsb,AdapterHub,['bibtex']
EMBO/sd-geneprod-roles-v2,EMBO,['doi.org/10.1038/nmeth.4471).']
cardiffnlp/roberta-base-tweet-topic-single-all,cardiffnlp,['bibtex']
torchxrayvision/densenet121-res224-rsna,torchxrayvision,"['arxiv.org/abs/2111.00595', 'bibtex']"
google/multiberts-seed_10,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
IDEA-CCNL/Erlangshen-ZEN2-668M-Chinese,IDEA-CCNL,"['arxiv.org/abs/2105.01279', 'bibtex']"
microsoft/unispeech-sat-base-sd,microsoft,['arxiv.org/abs/2110.05752']
flax-sentence-embeddings/multi-qa_v1-mpnet-mean_cos,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
ai4bharat/MultiIndicParaphraseGeneration,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
facebook/roberta-hate-speech-dynabench-r3-target,facebook,"['arxiv.org/abs/2012.15761', {'title': 'Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection'}]"
Narsil/gpt2,Narsil,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
Helsinki-NLP/opus-mt-tc-big-gmq-ar,Helsinki-NLP,['bibtex']
sail/poolformer_s24,sail,"['arxiv.org/abs/2111.11418', {'title': 'MetaFormer is Actually What You Need for Vision'}]"
gchhablani/fnet-base-finetuned-qnli,gchhablani,['arxiv.org/abs/2105.03824']
aajrami/bert-rand-base,aajrami,"['arxiv.org/abs/2203.10415', {'title': 'How does the pre-training objective affect what large language models learn about linguistic properties?'}]"
google/multiberts-seed_5,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
sail/poolformer_s36,sail,"['arxiv.org/abs/2111.11418', {'title': 'MetaFormer is Actually What You Need for Vision'}]"
espnet/kan-bayashi_jsut_tacotron2_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
BSC-TeMU/roberta-base-bne-capitel-ner,BSC-TeMU,['arxiv.org/abs/1907.11692']
Geotrend/bert-base-sw-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
ratishsp/Centrum,ratishsp,['arxiv.org/abs/2208.01006']
hyesunyun/update-summarization-led-edit-at-a-time,hyesunyun,['bibtex']
espnet/kan-bayashi_vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
awesometeng/TGL-3,awesometeng,['arxiv.org/abs/1910.10683']
fusing/ddpm-celeba-hq,fusing,['arxiv.org/abs/2006.11239']
drhyrum/bert-tiny-torch-vuln,drhyrum,"['arxiv.org/abs/1908.08962', 'bibtex']"
hfl/chinese-legal-electra-small-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
flax-community/indonesian-roberta-large,flax-community,['arxiv.org/abs/1907.11692']
Hate-speech-CNERG/deoffxlmr-mono-kannada,Hate-speech-CNERG,['bibtex']
Geotrend/distilbert-base-en-nl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
sarnikowski/electra-small-discriminator-da-256-cased,sarnikowski,['arxiv.org/abs/2003.10555']
l3cube-pune/hate-multi-roberta-hasoc-hindi,l3cube-pune,"['arxiv.org/abs/2110.12200', {'title': 'Hate and Offensive Speech Detection in Hindi and Marathi'}]"
rohitsroch/hybrid_hbh_bart-base_icsi_sum,rohitsroch,"['doi.org/10.1145/3508546.3508640*', 'bibtex']"
espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
hyunwoongko/reddit-9B,hyunwoongko,['arxiv.org/abs/1907.06616']
google/multiberts-seed_12,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
pierreguillou/byt5-small-qa-squad-v1.1-portuguese,pierreguillou,"['arxiv.org/abs/1907.06292', {'title': 'Portuguese ByT5 small QA (Question Answering), finetuned on SQUAD v1.1'}]"
espnet/aishell2_transducer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
qanastek/pos-french-camembert-flair,qanastek,"['arxiv.org/abs/1911.03894', 'bibtex']"
google/multiberts-seed_18,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
Davlan/afro-xlmr-large,Davlan,['arxiv.org/abs/2204.06487']
nthakur/mcontriever-base-msmarco,nthakur,['arxiv.org/abs/2112.09118']
espnet/kan-bayashi_jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_p-truncated-66d5fc,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Matthijs/mobilenet_v1_0.75_192,Matthijs,['arxiv.org/abs/1704.04861']
dbmdz/bert-small-historic-multilingual-cased,dbmdz,['arxiv.org/abs/1908.08962']
espnet/Shinji_Watanabe_open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_tacotron2_accent,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-conll2000,AdapterHub,['bibtex']
tner/deberta-large-wnut2017,tner,['bibtex']
AdapterHub/roberta-base-pf-mnli,AdapterHub,['bibtex']
airesearch/wangchanberta-base-wiki-syllable,airesearch,['arxiv.org/abs/1907.11692']
Finnish-NLP/t5-mini-nl8-finnish,Finnish-NLP,['arxiv.org/abs/1910.10683']
LeBenchmark/wav2vec2-FR-1K-large,LeBenchmark,[{'title': 'LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech'}]
mys/electra-base-turkish-cased-ner,mys,['doi.org/10.17632/cdcztymf4k.1']
tau/splinter-large-qass,tau,['bibtex']
xdai/mimic_longformer_base,xdai,['arxiv.org/abs/2204.06683']
yanaiela/roberta-base-epoch_79,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
espnet/GunnarThor_talromur_f_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
course5i/SEAD-L-6_H-256_A-8-qqp,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
gonced8/pegasus-conversational-qa,gonced8,['doi.org/[not']
bigscience/bloom-7b1-intermediate,bigscience,['arxiv.org/abs/1909.08053']
jkang/espnet2_librispeech_100_conformer_char,jkang,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
LeBenchmark/wav2vec-FR-1K-Female-base,LeBenchmark,"['arxiv.org/abs/2204.01397', {'title': 'A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems'}]"
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
ctoraman/RoBERTa-TR-medium-morph-44k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
hfl/chinese-electra-large-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
TalTechNLP/espnet2_estonian,TalTechNLP,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-luo,mbeukman,['arxiv.org/abs/2103.11811']
jcblaise/electra-tagalog-base-cased-generator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
darragh/swinunetr-btcv-tiny,darragh,[{'title': 'Self-supervised pre-training of swin transformers for 3d medical image analysis'}]
lewtun/dummy-setfit-model,lewtun,"['arxiv.org/abs/1908.10084', 'bibtex']"
espnet/kan-bayashi_jsut_conformer_fastspeech2_transformer_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-rte,AdapterHub,['bibtex']
google/multiberts-seed_11,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
espnet/chai_librispeech_asr_train_conformer-rnn_transducer_raw_en_bpe5000_sp,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
espnet/bn_openslr53,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
microsoft/xclip-large-patch14-16-frames,microsoft,['arxiv.org/abs/2208.02816']
course5i/SEAD-L-6_H-384_A-12-qqp,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
tals/albert-base-vitaminc_rationale,tals,['bibtex']
Goud/AraBERT-summarization-goud,Goud,[{'title': 'Goud.ma: a News Article Dataset for Summarization in Moroccan Darija'}]
tftransformers/bert-base-cased,tftransformers,"['arxiv.org/abs/1810.04805', 'bibtex']"
cardiffnlp/roberta-large-tweet-topic-multi-all,cardiffnlp,['bibtex']
Helsinki-NLP/opus-mt-tc-big-zle-de,Helsinki-NLP,['bibtex']
UMCU/RobBERT_NegationDetection_32xTokenWindow,UMCU,"['arxiv.org/abs/2209.00470', 'doi.org/10.18653/v1/2020.findings-emnlp.292.']"
naver/splade-cocondenser-selfdistil,naver,"['arxiv.org/abs/2205.04733', 'doi.org/10.48550/arxiv.2205.04733,']"
jcblaise/gpt2-tagalog,jcblaise,[{'title': '{Localization of Fake News Detection via Multitask Transfer Learning'}]
Davlan/naija-twitter-sentiment-afriberta-large,Davlan,"['arxiv.org/abs/2201.08277', {'title': 'NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis'}]"
tner/twitter-roberta-base-dec2020-tweetner7-2021,tner,['bibtex']
facebook/spar-wiki-bm25-lexmodel-context-encoder,facebook,['arxiv.org/abs/2110.06918']
cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all,cardiffnlp,['bibtex']
espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
rsuwaileh/IDRISI-LMR-EN-random-typebased,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
espnet/kan-bayashi_vctk_xvector_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jacon-truncated-f45dcb,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_libritts_xvector_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
jkang/espnet2_librispeech_100_conformer_word,jkang,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-qnli,AdapterHub,['bibtex']
AdapterHub/bert-base-uncased-pf-anli_r3,AdapterHub,['bibtex']
Sakonii/deberta-base-nepali,Sakonii,['arxiv.org/abs/1911.02116']
baffo32/gpt-j-6B-ptmap,baffo32,['arxiv.org/abs/2104.09864']
espnet/kan-bayashi_vctk_gst_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Langboat/mengzi-oscar-base-retrieval,Langboat,['arxiv.org/abs/2110.06696']
espnet/kan-bayashi_jsut_conformer_fastspeech2_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
pyf98/aishell_conformer_e12_amp,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
course5i/SEAD-L-6_H-256_A-8-rte,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
espnet/kan-bayashi_ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
course5i/SEAD-L-6_H-384_A-12-mrpc,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
espnet/kan-bayashi_ljspeech_tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
pyronear/rexnet1_5x,pyronear,"['arxiv.org/abs/2007.00992', 'bibtex']"
csebuetnlp/banglishbert,csebuetnlp,"['arxiv.org/abs/2101.00204', {'title': 'BanglaBERT: Lagnuage Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla'}]"
google/multiberts-seed_14,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
course5i/SEAD-L-6_H-384_A-12-rte,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
tals/albert-base-vitaminc_wnei-fever,tals,['bibtex']
google/multiberts-seed_19,google,"['arxiv.org/abs/2106.16163', {'title': 'The MultiBERTs: BERT Reproductions for Robustness Analysis'}]"
jonahank/KlimaBERT,jonahank,['arxiv.org/abs/1810.04805']
Geotrend/bert-base-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/kan-bayashi_jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ctoraman/RoBERTa-TR-medium-morph-16k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ibraheemmoosa/xlmindic-base-multiscript-soham,ibraheemmoosa,[{'title': 'Does Transliteration Help Multilingual Language Modeling?'}]
Helsinki-NLP/opus-mt-tc-big-sh-en,Helsinki-NLP,['bibtex']
w11wo/malaysian-distilbert-small,w11wo,['arxiv.org/abs/1910.01108']
unicamp-dl/mMiniLM-L6-v2-pt-msmarco-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
course5i/SEAD-L-6_H-256_A-8-wnli,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
tner/deberta-v3-large-tweebank-ner,tner,['bibtex']
espnet/kan-bayashi_vctk_tts_train_gst_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
course5i/SEAD-L-6_H-256_A-8-mrpc,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
tner/bert-large-tweetner7-all,tner,['bibtex']
Geotrend/bert-base-pl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
aioxlabs/tacotron-swahili,aioxlabs,['arxiv.org/abs/1712.05884']
AdapterHub/bert-base-uncased-pf-mrpc,AdapterHub,['bibtex']
google/ddpm-ema-church-256,google,['arxiv.org/abs/2006.11239']
course5i/SEAD-L-6_H-384_A-12-wnli,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
AdapterHub/bert-base-uncased-pf-stsb,AdapterHub,['bibtex']
facebook/wav2vec2-base-sv-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
course5i/SEAD-L-6_H-256_A-8-qnli,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
eugenesiow/carn,eugenesiow,"['arxiv.org/abs/1803.08664', {'title': 'Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network'}]"
espnet/kan-bayashi_vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-sst2,AdapterHub,['bibtex']
AdapterHub/roberta-base-pf-rte,AdapterHub,['bibtex']
torchxrayvision/densenet121-res224-nih,torchxrayvision,"['arxiv.org/abs/2111.00595', 'bibtex']"
Geotrend/bert-base-en-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
GunnarThor/talromur_f_tacotron2,GunnarThor,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
course5i/SEAD-L-6_H-384_A-12-qnli,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
AdapterHub/bert-base-uncased-pf-drop,AdapterHub,['bibtex']
espnet/kan-bayashi_libritts_gst_xvector_trasnformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
EventMiner/bigbird-roberta-large-en-doc,EventMiner,['bibtex']
espnet/GunnarThor_talromur_c_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AlekseyKorshuk/test,AlekseyKorshuk,['bibtex']
it5/it5-small-formal-to-informal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
pile-of-law/distilbert-base-uncased-finetuned-eoir_privacy,pile-of-law,['arxiv.org/abs/2207.00220']
glasses/resnet34,glasses,['arxiv.org/abs/1512.03385']
doc2query/msmarco-italian-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
espnet/aishell2_att_ctc_espnet2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
dragonSwing/viwav2vec2-base-100h,dragonSwing,['arxiv.org/abs/2006.11477']
espnet/kan-bayashi_ljspeech_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Finnish-NLP/convbert-base-finnish,Finnish-NLP,['arxiv.org/abs/2008.02496']
facebook/vit-msn-base-4,facebook,"['arxiv.org/abs/2204.07141', {'title': 'Masked Siamese Networks for Label-Efficient Learning'}]"
imohammad12/GRS-Constrained-Paraphrasing-Bart,imohammad12,['bibtex']
flax-sentence-embeddings/multi-qa_v1-MiniLM-L6-cls_dot,flax-sentence-embeddings,"['arxiv.org/abs/2102.07033', 'doi.org/10.18653/v1/p19-1346)']"
AdapterHub/bert-base-uncased-pf-fce_error_detection,AdapterHub,['bibtex']
leftthomas/resnet50,leftthomas,['arxiv.org/abs/1512.03385']
google/ddpm-bedroom-256,google,['arxiv.org/abs/2006.11239']
invokerliang/MWP-BERT-zh,invokerliang,[{'title': 'MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving'}]
espnet/fsc_challenge_slu_2pass_transformer_gt,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/fsc_challenge_slu_2pass_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/slurp_slu_2pass,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
mukayese/transformer-turkish-summarization,mukayese,['arxiv.org/abs/2203.01215']
neuropark/sahajBERT-NCC,neuropark,['bibtex']
keras-io/video-vision-transformer,keras-io,['arxiv.org/abs/2103.15691']
Helsinki-NLP/opus-mt-tc-big-eu-itc,Helsinki-NLP,['bibtex']
facebook/s2t-small-mustc-en-pt-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
facebook/s2t-small-covost2-en-ca-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
it5/mt5-base-question-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
espnet/kan-bayashi_jvs_jvs010_vits_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Nokia/nlgp-docstring,Nokia,['arxiv.org/abs/2108.05198']
tommy19970714/wav2vec2-base-960h,tommy19970714,['arxiv.org/abs/2006.11477']
AdapterHub/bert-base-uncased-pf-conll2003_pos,AdapterHub,['bibtex']
lichenda/chime4_fasnet_dprnn_tac,lichenda,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-quoref,AdapterHub,['bibtex']
AdapterHub/bert-base-uncased-pf-conll2003,AdapterHub,['bibtex']
facebook/s2t-small-mustc-en-ro-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
asapp/sew-d-small-100k,asapp,['arxiv.org/abs/2109.06870']
flax-community/medclip,flax-community,['bibtex']
aapot/wav2vec2-xlsr-300m-finnish,aapot,['arxiv.org/abs/2111.09296']
espnet/GunnarThor_talromur_h_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/s2t-small-mustc-en-nl-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
cwkeam/m-ctc-t-large-sequence-lid,cwkeam,"['arxiv.org/abs/2111.00161', {'title': 'Pseudo-Labeling for Massively Multilingual Speech Recognition'}]"
Davlan/distilbert-base-multilingual-cased-masakhaner,Davlan,"['arxiv.org/abs/2103.11811', {'title': 'Masakha{NER'}]"
MultiBertGunjanPatrick/multiberts-seed-9,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
espnet/kan-bayashi_csmsc_full_band_vits,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
mudes/en-base,mudes,"['arxiv.org/abs/2102.09665', {'title': '{MUDES: Multilingual Detection of Offensive Spans'}]"
pyf98/librispeech_conformer,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
asafaya/albert-base-arabic,asafaya,['doi.org/10.5281/zenodo.4718724}']
Hate-speech-CNERG/deoffxlmr-mono-malyalam,Hate-speech-CNERG,['bibtex']
espnet/kamo-naoyuki_hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20-truncated-934e17,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
gchhablani/fnet-base-finetuned-qqp,gchhablani,['arxiv.org/abs/2105.03824']
ibraheemmoosa/xlmindic-base-uniscript,ibraheemmoosa,[{'title': 'Does Transliteration Help Multilingual Language Modeling?'}]
AdapterHub/bert-base-uncased-pf-multirc,AdapterHub,['bibtex']
AdapterHub/roberta-base-pf-drop,AdapterHub,['bibtex']
microsoft/xclip-base-patch16-ucf-16-shot,microsoft,['arxiv.org/abs/2208.02816']
rufimelo/Legal-BERTimbau-sts-base-ma-v2,rufimelo,['bibtex']
CommunityLM/democrat-twitter-gpt2,CommunityLM,"['arxiv.org/abs/2209.07065', 'bibtex']"
facebook/wav2vec2-base-nl-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-portuguese,Edresson,['arxiv.org/abs/2204.00618']
keras-io/Object-Detection-RetinaNet,keras-io,['arxiv.org/abs/1708.02002']
flax-community/wav2vec2-german,flax-community,['arxiv.org/abs/2006.11477']
nielsr/tapex-large,nielsr,['arxiv.org/abs/2107.07653']
mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
glasses/resnet26d,glasses,['arxiv.org/abs/1512.03385']
Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa,Intel,['arxiv.org/abs/2111.05754']
AdapterHub/roberta-base-pf-race,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese,Edresson,['arxiv.org/abs/2204.00618']
keras-io/vit-small-ds,keras-io,['arxiv.org/abs/2010.11929']
tau/bart-large-sled-govreport,tau,"['arxiv.org/abs/2104.02112', {'title': 'Efficient Long-Text Understanding with Short-Text Models'}]"
facebook/wav2vec2-base-fi-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
nvidia/nemo-megatron-gpt-1.3B,nvidia,['arxiv.org/abs/2101.00027']
l3cube-pune/hate-bert-hasoc-marathi,l3cube-pune,"['arxiv.org/abs/2110.12200', {'title': 'Hate and Offensive Speech Detection in Hindi and Marathi'}]"
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_r-truncated-f43d8f,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Krystalan/mdialbart_zh,Krystalan,['arxiv.org/abs/2202.05599']
espnet/americasnlp22-asr-quy,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
NDugar/3epoch-3large,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
jcblaise/bert-tagalog-base-cased-WWM,jcblaise,[{'title': 'Establishing Baselines for Text Classification in Low-Resource Languages'}]
asafaya/albert-large-arabic,asafaya,['doi.org/10.5281/zenodo.4718724}']
AdapterHub/bert-base-uncased-pf-quartz,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
google/ncsnpp-bedroom-256,google,['arxiv.org/abs/2011.13456']
Geotrend/distilbert-base-en-fr-es-pt-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Davlan/xlm-roberta-large-masakhaner,Davlan,"['arxiv.org/abs/2103.11811', {'title': 'Masakha{NER'}]"
PaulTran/vietnamese_essay_identify,PaulTran,"['arxiv.org/abs/2003.00744', {'title': '{PhoBERT: Pre-trained language models for Vietnamese'}]"
keshan/sinhala-roberta-oscar,keshan,['arxiv.org/abs/1907.11692']
asapp/sew-d-mid-k127-100k,asapp,['arxiv.org/abs/2109.06870']
uer/chinese_roberta_L-10_H-768,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
gchhablani/fnet-base-finetuned-wnli,gchhablani,['arxiv.org/abs/2105.03824']
jcblaise/bert-tagalog-base-uncased-WWM,jcblaise,[{'title': 'Establishing Baselines for Text Classification in Low-Resource Languages'}]
Geotrend/distilbert-base-en-fr-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
eesungkim/stt_kr_conformer_transducer_large,eesungkim,['arxiv.org/abs/2005.08100']
ITESM/st_demo_5,ITESM,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
GroNLP/bert-base-dutch-cased-frisian,GroNLP,['arxiv.org/abs/2105.02855']
mrm8488/spanbert-base-finetuned-squadv1,mrm8488,['arxiv.org/abs/1907.10529']
allenai/mtk-instruct-3b-def-pos,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
NDugar/v3large-2epoch,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
espnet/kan-bayashi_vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/bert-base-en-sw-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-en-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
unicamp-dl/mt5-base-mmarco-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
bookbot/distil-wav2vec2-adult-child-cls-37m,bookbot,['arxiv.org/abs/2006.11477']
nielsr/beit-large-patch16-224-pt22k-ft22k,nielsr,['arxiv.org/abs/2106.08254']
eugenesiow/awsrn-bam,eugenesiow,"['arxiv.org/abs/1904.02358', {'title': 'Lightweight Image Super-Resolution with Adaptive Weighted Learning Network'}]"
hackathon-pln-es/electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas,hackathon-pln-es,['bibtex']
it5/it5-base-formal-to-informal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
AdapterHub/bert-base-uncased-pf-ud_pos,AdapterHub,['bibtex']
dbmdz/electra-base-turkish-mc4-cased-generator,dbmdz,['doi.org/10.5281/zenodo.3770924}']
racai/distilbert-multi-base-romanian-cased,racai,"['arxiv.org/abs/2112.12650', {'title': 'Distilling the Knowledge of Romanian BERTs Using Multiple Teachers'}]"
tftransformers/bert-base-uncased,tftransformers,"['arxiv.org/abs/1810.04805', 'bibtex']"
marcosgg/bert-small-gl-cased,marcosgg,"['arxiv.org/abs/2106.13553', 'bibtex']"
alexanderfalk/danbert-small-cased,alexanderfalk,['bibtex']
mbeukman/xlm-roberta-base-finetuned-ner-naija,mbeukman,['arxiv.org/abs/2103.11811']
PrimeQA/listqa_nq-task-xlm-roberta-large,PrimeQA,"['arxiv.org/abs/1911.02116', 'bibtex']"
cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020,cardiffnlp,['bibtex']
espnet/kan-bayashi_vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
NDugar/v3large-1epoch,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
mbeukman/xlm-roberta-base-finetuned-naija-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
DCU-NLP/bert-base-irish-cased-v1,DCU-NLP,['arxiv.org/abs/2107.12930']
it5/mt5-small-ilgiornale-to-repubblica,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
AdapterHub/roberta-base-pf-cq,AdapterHub,['bibtex']
course5i/SEAD-L-6_H-384_A-12-sst2,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
course5i/SEAD-L-6_H-256_A-8-sst2,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
unicamp-dl/mMiniLM-L6-v2-en-msmarco,unicamp-dl,['arxiv.org/abs/2108.13897']
aajrami/bert-fc-base,aajrami,"['arxiv.org/abs/2203.10415', {'title': 'How does the pre-training objective affect what large language models learn about linguistic properties?'}]"
Narsil/bart-large-mnli-opti,Narsil,['arxiv.org/abs/1910.13461']
google/tapas-mini-finetuned-tabfact,google,"['arxiv.org/abs/2010.00571', {'title': 'TabFact : A Large-scale Dataset for Table-based Fact Verification'}]"
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
NDugar/1epochv3,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
imohammad12/GRS-complex-simple-classifier-DeBerta,imohammad12,['bibtex']
TheRensselaerIDEA/gpt2-large-vaccine-tweet-response,TheRensselaerIDEA,['arxiv.org/abs/2204.04353']
Graphcore/bert-base-uncased,Graphcore,['arxiv.org/abs/1904.00962']
abhishek/deberta-v3-base-autotrain,abhishek,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
espnet/GunnarThor_talromur_d_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-pmb_sem_tagging,AdapterHub,['bibtex']
espnet/GunnarThor_talromur_g_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
BSC-TeMU/roberta-base-bne-capitel-pos,BSC-TeMU,['arxiv.org/abs/1907.11692']
asafaya/albert-xlarge-arabic,asafaya,['doi.org/10.5281/zenodo.4718724}']
torchxrayvision/densenet121-res224-chex,torchxrayvision,"['arxiv.org/abs/2111.00595', 'bibtex']"
course5i/SEAD-L-6_H-256_A-8-mnli,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm,Finnish-NLP,['arxiv.org/abs/2111.09296']
unicamp-dl/ptt5-base-en-pt-msmarco-100k-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
Geotrend/bert-base-en-tr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-d-nce,relbert,['bibtex']
espnet/kan-bayashi_vctk_gst_xvector_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
relbert/roberta-large-semeval2012-average-no-mask-prompt-a-nce-classification,relbert,['bibtex']
espnet/kan-bayashi_jsut_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10,Harveenchadha,['arxiv.org/abs/2107.07402']
relbert/roberta-large-semeval2012-average-no-mask-prompt-a-nce,relbert,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-1-100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
globuslabs/ScholarBERT_100_WB,globuslabs,['arxiv.org/abs/2205.11342']
microsoft/xclip-base-patch16-ucf-4-shot,microsoft,['arxiv.org/abs/2208.02816']
MultiBertGunjanPatrick/multiberts-seed-7,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/bert-base-en-th-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
glasses/resnet50,glasses,['arxiv.org/abs/1512.03385']
l3cube-pune/marathi-albert-v2,l3cube-pune,['arxiv.org/abs/2202.01159']
microsoft/xclip-base-patch16,microsoft,['arxiv.org/abs/2208.02816']
facebook/data2vec-audio-base-100h,facebook,['arxiv.org/abs/2202.03555']
microsoft/unihanlm-base,microsoft,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce,relbert,['bibtex']
IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese,IDEA-CCNL,['bibtex']
izumi-lab/electra-small-paper-japanese-generator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce,relbert,['bibtex']
espnet/kan-bayashi_vctk_xvector_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
kornosk/polibertweet-political-twitter-roberta-mlm-small,kornosk,[{'title': 'PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter'}]
it5/mt5-base-question-answering,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
nvidia/nemo-megatron-t5-3B,nvidia,['arxiv.org/abs/1910.10683']
google/t5-efficient-small-dl16,google,['arxiv.org/abs/2109.10686']
espnet/kan-bayashi_ljspeech_tts_train_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/spar-marco-unicoil-lexmodel-query-encoder,facebook,['arxiv.org/abs/2110.06918']
patrickvonplaten/data2vec-base,patrickvonplaten,['arxiv.org/abs/2202.03555']
gchhablani/fnet-base-finetuned-mrpc,gchhablani,['arxiv.org/abs/2105.03824']
pucpr/biobertpt-bio,pucpr,['bibtex']
w11wo/lao-roberta-base,w11wo,['arxiv.org/abs/1907.11692']
espnet/kan-bayashi_libritts_xvector_trasnformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/Shinji_Watanabe_spgispeech_asr_train_asr_conformer6_n_fft512_hop_lengt-truncated-f1ac86,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ctoraman/RoBERTa-TR-medium-wp-66k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
izumi-lab/electra-small-paper-japanese-fin-discriminator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/spar-wiki-bm25-lexmodel-query-encoder,facebook,['arxiv.org/abs/2110.06918']
google/t5-efficient-small-ff3000,google,['arxiv.org/abs/2109.10686']
gchhablani/fnet-base-finetuned-rte,gchhablani,['arxiv.org/abs/2105.03824']
tner/bert-large-tweetner7-random,tner,['bibtex']
pyf98/librispeech_100h_transformer,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_libritts_tts_train_xvector_conformer_fastspeech2_transform-truncated-42b443,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
superb/wav2vec2-large-superb-sid,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
espnet/kan-bayashi_ljspeech_tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-wikihop,AdapterHub,['bibtex']
crumb/gpt-j-6b-shakespeare,crumb,['arxiv.org/abs/2101.00027']
qarib/bert-base-qarib60_1970k,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
benjamin/gpt2-wechsel-swahili,benjamin,['bibtex']
l3cube-pune/hing-gpt-devanagari,l3cube-pune,['arxiv.org/abs/2204.08398']
tner/bertweet-base-tweetner7-continuous,tner,['bibtex']
espnet/kan-bayashi_jsut_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
flax-community/gpt-neo-1.3B-apps,flax-community,['arxiv.org/abs/2107.03374']
keras-io/shiftvit,keras-io,['arxiv.org/abs/2201.10801']
espnet/kan-bayashi_csmsc_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Bingsu/vitB32_bert_ko_small_clip,Bingsu,['arxiv.org/abs/2004.09813']
superb/hubert-large-superb-ks,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
facebook/spar-marco-unicoil-lexmodel-context-encoder,facebook,['arxiv.org/abs/2110.06918']
probing-vits/vit-dino-base16,probing-vits,['arxiv.org/abs/2104.14294']
unicamp-dl/mt5-base-en-pt-msmarco-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
julien-c/timm-dpn92,julien-c,['arxiv.org/abs/1707.01629']
tner/twitter-roberta-base-dec2020-tweetner7-all,tner,['bibtex']
google/t5-efficient-small-dl2,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-kv128,google,['arxiv.org/abs/2109.10686']
tner/bertweet-base-tweetner7-all,tner,['bibtex']
AdapterHub/bert-base-uncased-pf-trec,AdapterHub,['bibtex']
it5/mt5-small-news-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
aliosm/ComVE-gpt2-medium,aliosm,[{'title': 'JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation'}]
espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-sick,AdapterHub,['bibtex']
keras-io/adamatch-domain-adaption,keras-io,['arxiv.org/abs/1605.07146']
tner/twitter-roberta-base-2019-90m-tweetner7-random,tner,['bibtex']
espnet/kan-bayashi_jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jac-truncated-60fc24,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
andrejmiscic/simcls-scorer-xsum,andrejmiscic,"['arxiv.org/abs/2106.01890', 'bibtex']"
gchhablani/fnet-base-finetuned-mnli,gchhablani,['arxiv.org/abs/2105.03824']
mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
keras-io/involution,keras-io,['arxiv.org/abs/2103.06255']
StevenLimcorn/MelayuBERT,StevenLimcorn,['arxiv.org/abs/1810.04805']
Muennighoff/SGPT-1.3B-mean-nli,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
espnet/kan-bayashi_jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jacon-truncated-e5d906,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
frgfm/darknet53,frgfm,"['arxiv.org/abs/1804.02767', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_r-truncated-35ef5a,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_transformer_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/bert-base-en-hi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/kan-bayashi_vctk_xvector_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
HueyNemud/das22-43-camembert_pretrained_finetuned_pero,HueyNemud,['doi.org/10.1007/978-3-031-06555-2_30']
UWB-AIR/Czert-B-base-cased-long-zero-shot,UWB-AIR,"['arxiv.org/abs/2103.13031', {'title': 'Czert -- Czech BERT-like Model for Language Representation'}]"
AdapterHub/roberta-base-pf-ud_deprel,AdapterHub,['bibtex']
HueyNemud/das22-42-camembert_finetuned_ref,HueyNemud,['doi.org/10.1007/978-3-031-06555-2_30']
HueyNemud/das22-41-camembert_pretrained_finetuned_ref,HueyNemud,['doi.org/10.1007/978-3-031-06555-2_30']
tner/deberta-v3-large-bc5cdr,tner,['bibtex']
AdapterHub/bert-base-uncased-pf-scicite,AdapterHub,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-d-nce-classification,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce-classification,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce-classification,relbert,['bibtex']
jcblaise/electra-tagalog-base-uncased-generator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
bigscience/bloom-560m-intermediate,bigscience,['arxiv.org/abs/1909.08053']
unicamp-dl/mMiniLM-L6-v2-mmarco-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
Geotrend/bert-base-en-es-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
neuralmagic/mobilebert-uncased-finetuned-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
microsoft/unispeech-1350-en-17h-ky-ft-1h,microsoft,['arxiv.org/abs/2101.07597']
rmihaylov/bert-base-squad-theseus-bg,rmihaylov,['arxiv.org/abs/1810.04805']
espnet/siddhana_slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
readerbench/jurBERT-large,readerbench,[{'title': 'jurBERT: A Romanian BERT Model for Legal Judgement Prediction'}]
facebook/xm_transformer_600m-en_vi-multi_domain,facebook,"['arxiv.org/abs/2010.05171', 'bibtex']"
espnet/GunnarThor_talromur_d_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
mrm8488/bert-multi-uncased-finetuned-xquadv1,mrm8488,['bibtex']
espnet/russian_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
glasses/resnet50d,glasses,['arxiv.org/abs/1512.03385']
glasses/resnet18,glasses,['arxiv.org/abs/1512.03385']
ai4bharat/MultiIndicWikiBioUnified,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
Geotrend/distilbert-base-en-el-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
jkang/espnet2_an4_transformer,jkang,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tftransformers/bart-large,tftransformers,"['arxiv.org/abs/1910.13461', 'bibtex']"
noharm-ai/anony,noharm-ai,[{'title': 'De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier'}]
p208p2002/bart-squad-nqg-hl,p208p2002,['arxiv.org/abs/1606.05250']
sismetanin/mbart_ru_sum_gazeta-ru-sentiment-rureviews,sismetanin,['bibtex']
mrm8488/spanbert-large-finetuned-tacred,mrm8488,['arxiv.org/abs/1907.10529']
MultiBertGunjanPatrick/multiberts-seed-2-0k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/kamo-naoyuki_aishell_conformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
microsoft/reacc-py-retriever,microsoft,['arxiv.org/abs/2203.07722']
unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
espnet/GunnarThor_talromur_g_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
racai/e4a-covid-question-answering,racai,"['arxiv.org/abs/2206.08046', {'title': 'An Open-Domain QA System for e-Governance'}]"
globuslabs/ScholarBERT_10_WB,globuslabs,['arxiv.org/abs/2205.11342']
tftransformers/gpt2,tftransformers,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
it5/mt5-small-headline-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
facebook/s2t-small-covost2-en-de-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
sebastian-hofstaetter/colberter-128-32-msmarco,sebastian-hofstaetter,"['arxiv.org/abs/2203.13088', 'bibtex']"
CommunityLM/republican-twitter-gpt2,CommunityLM,"['arxiv.org/abs/2209.07065', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
biu-nlp/alephbert-base,biu-nlp,['arxiv.org/abs/1810.04805']
espnet/arabic_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
rohitsroch/hybrid_utt-clusterrank_bart-base_dialogsum_sum,rohitsroch,"['doi.org/10.1145/3508546.3508640*', 'bibtex']"
espnet/pt_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
microsoft/xclip-base-patch16-hmdb-4-shot,microsoft,['arxiv.org/abs/2208.02816']
tner/deberta-v3-large-ttc,tner,['bibtex']
nvidia/stt_rw_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
AdapterHub/bert-base-uncased-pf-wic,AdapterHub,['bibtex']
fbaigt/proc_roberta,fbaigt,"['arxiv.org/abs/2109.04711', 'bibtex']"
Muennighoff/SBERT-base-nli-v2,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
Finnish-NLP/t5-tiny-nl6-finnish,Finnish-NLP,['arxiv.org/abs/1910.10683']
espnet/GunnarThor_talromur_a_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/s2t-small-covost2-en-et-st,facebook,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
AdapterHub/roberta-base-pf-wnut_17,AdapterHub,['bibtex']
phjhk/hklegal-xlm-r-large,phjhk,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
it5/it5-large-formal-to-informal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
mbeukman/xlm-roberta-base-finetuned-ner-yoruba,mbeukman,['arxiv.org/abs/2103.11811']
AdapterHub/bert-base-uncased-pf-hotpotqa,AdapterHub,['bibtex']
espnet/ftshijt_espnet2_asr_dsing_hubert_conformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tals/albert-xlarge-vitaminc-fever,tals,['bibtex']
LeBenchmark/wav2vec-FR-1K-Male-base,LeBenchmark,"['arxiv.org/abs/2204.01397', {'title': 'A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems'}]"
jkang/espnet2_an4_asr,jkang,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/bert-base-en-fr-de-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-en-pl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/GunnarThor_talromur_b_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_csmsc_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
course5i/SEAD-L-6_H-256_A-8-stsb,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
course5i/SEAD-L-6_H-384_A-12-stsb,course5i,"['arxiv.org/abs/1910.01108', 'bibtex']"
espnet/kan-bayashi_jsut_fastspeech2_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/distilbert-base-en-fr-lt-no-pl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-yoruba,mbeukman,['arxiv.org/abs/2103.11811']
MCG-NJU/videomae-large,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
facebook/wav2vec2-large-10k-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
abhi1nandy2/Europarl-roberta-base,abhi1nandy2,['bibtex']
frgfm/resnet18,frgfm,"['arxiv.org/abs/1512.03385', 'bibtex']"
keras-io/WGAN-GP,keras-io,['arxiv.org/abs/1701.07875']
fusing/ddpm-cifar10-ema,fusing,['arxiv.org/abs/2006.11239']
superb/wav2vec2-large-superb-ks,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
AdapterHub/bert-base-uncased-pf-mit_movie_trivia,AdapterHub,['bibtex']
microsoft/unispeech-1350-en-90-it-ft-1h,microsoft,['arxiv.org/abs/2101.07597']
guidecare/all-mpnet-base-v2-feature-extraction,guidecare,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
Muennighoff/SGPT-2.7B-weightedmean-msmarco-specb-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
keras-io/convmixer,keras-io,['arxiv.org/abs/2201.09792']
google/t5-efficient-small-dm128,google,['arxiv.org/abs/2109.10686']
glasses/resnet152,glasses,['arxiv.org/abs/1512.03385']
frgfm/repvgg_a1,frgfm,"['arxiv.org/abs/2101.03697', 'bibtex']"
yanaiela/roberta-base-epoch_70,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
classla/bcms-bertic-parlasent-bcs-bi,classla,"['arxiv.org/abs/2206.00929', 'doi.org/10.48550/arxiv.2206.00929,', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-1500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
yanaiela/roberta-base-epoch_31,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
espnet/kan-bayashi_jsut_vits_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/bert-base-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
doc2query/msmarco-indonesian-mt5-base-v1,doc2query,['arxiv.org/abs/1904.08375']
questgen/msmarco-distilbert-base-v4-feature-extraction-pipeline,questgen,"['arxiv.org/abs/1908.10084', 'bibtex']"
SenseTime/deformable-detr-single-scale-dc5,SenseTime,"['arxiv.org/abs/2010.04159', 'doi.org/10.48550/arxiv.2010.04159,']"
tftransformers/albert-base-v1,tftransformers,"['arxiv.org/abs/1909.11942', 'bibtex']"
Finnish-NLP/convbert-base-generator-finnish,Finnish-NLP,['arxiv.org/abs/2008.02496']
glasses/resnet26,glasses,['arxiv.org/abs/1512.03385']
microsoft/xclip-base-patch16-ucf-2-shot,microsoft,['arxiv.org/abs/2208.02816']
yanaiela/roberta-base-epoch_80,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
microsoft/xclip-base-patch16-hmdb-2-shot,microsoft,['arxiv.org/abs/2208.02816']
keras-io/randaugment,keras-io,['arxiv.org/abs/1909.13719']
cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all,cardiffnlp,['bibtex']
cardiffnlp/roberta-base-tweet-topic-multi-2020,cardiffnlp,['bibtex']
espnet/kan-bayashi_jvs_jvs001_vits_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-180k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
gchhablani/fnet-base-finetuned-cola,gchhablani,['arxiv.org/abs/2105.03824']
Geotrend/bert-base-ja-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
google/t5-efficient-large-el6,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-mini-nl6,google,['arxiv.org/abs/2109.10686']
AdapterHub/roberta-base-pf-commonsense_qa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
Geotrend/bert-base-en-fr-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
AdapterHub/bert-base-uncased-pf-cosmos_qa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
Helsinki-NLP/opus-mt-tc-big-zle-zlw,Helsinki-NLP,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-3,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
tftransformers/bert-large-uncased,tftransformers,"['arxiv.org/abs/1810.04805', 'bibtex']"
keras-io/GauGAN-Image-generation,keras-io,['arxiv.org/abs/1903.07291']
gchhablani/fnet-base-finetuned-stsb,gchhablani,['arxiv.org/abs/2105.03824']
AdapterHub/bert-base-uncased-pf-hellaswag,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
rohitsroch/hybrid_hbh_t5-small_ami_sum,rohitsroch,"['doi.org/10.1145/3508546.3508640*', 'bibtex']"
niklaspm/linkbert-large-finetuned-squad,niklaspm,['arxiv.org/abs/2203.15827']
m3hrdadfi/albert-fa-base-v2-sentiment-deepsentipers-multi,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
MultiBertGunjanPatrick/multiberts-seed-0-300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
projecte-aina/roberta-base-ca-cased-pos,projecte-aina,"['arxiv.org/abs/1907.11692', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof,mbeukman,['arxiv.org/abs/2103.11811']
relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce,relbert,['bibtex']
Geotrend/bert-base-el-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-en-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
leonardvorbeck/wav2vec2-large-robust-SB300,leonardvorbeck,['arxiv.org/abs/2104.01027']
mudes/en-large,mudes,"['arxiv.org/abs/2102.09665', {'title': '{MUDES: Multilingual Detection of Offensive Spans'}]"
mrm8488/bloom-6b3-8bit,mrm8488,['arxiv.org/abs/2106.09685']
google/t5-efficient-small-dl4,google,['arxiv.org/abs/2109.10686']
crumb/gpt-j-6b-finetune-super-glue,crumb,['arxiv.org/abs/2110.02861']
unicamp-dl/mMiniLM-L6-v2-en-pt-msmarco-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
aapot/wav2vec2-xlsr-300m-finnish-lm,aapot,['arxiv.org/abs/2111.09296']
MultiBertGunjanPatrick/multiberts-seed-4,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
nvidia/stt_ca_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
mbeukman/xlm-roberta-base-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
jcblaise/electra-tagalog-small-cased-discriminator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
espnet/kamo-naoyuki_chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
rsuwaileh/IDRISI-LMR-EN-random-typeless,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
EleutherAI/enformer-corr_coef_obj,EleutherAI,['doi.org/10.1038/s41592-021-01252-x']
pyf98/librispeech_100h_conformer,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
jirmauritz/robbert-v2-dutch-base,jirmauritz,"['arxiv.org/abs/2001.06286', 'bibtex']"
lgris/bp500-base100k_voxpopuli,lgris,['arxiv.org/abs/2012.03411']
byan/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp,byan,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
EMBO/sd-smallmol-roles-v2,EMBO,['doi.org/10.1038/nmeth.4471).']
ucberkeley-dlab/hate-measure-roberta-large,ucberkeley-dlab,"['arxiv.org/abs/2009.10277', {'title': 'Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application'}]"
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-naija,mbeukman,['arxiv.org/abs/2103.11811']
mbeukman/xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m,bookbot,['arxiv.org/abs/2111.09296']
MultiBertGunjanPatrick/multiberts-seed-2,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
ufal/byt5-small-multilexnorm2021-nl,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
cwkeam/mctct-large,cwkeam,"['arxiv.org/abs/2111.00161', {'title': 'Pseudo-Labeling for Massively Multilingual Speech Recognition'}]"
edbeeching/decision-transformer-gym-walker2d-medium,edbeeching,['arxiv.org/abs/2106.01345']
aapot/wav2vec2-xlsr-1b-finnish-lm,aapot,['arxiv.org/abs/2111.09296']
yhavinga/t5-eff-large-8l-dutch-english-cased,yhavinga,['arxiv.org/abs/2109.10686']
piEsposito/braquad-bert-qna,piEsposito,['bibtex']
unicamp-dl/mMiniLM-L6-v2-pt-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
shalpin87/dialoGPT-homer-simpson,shalpin87,['arxiv.org/abs/1911.00536']
Hate-speech-CNERG/deoffxlmr-mono-tamil,Hate-speech-CNERG,['bibtex']
AdapterHub/bert-base-uncased-pf-copa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
globuslabs/ScholarBERT_1,globuslabs,['arxiv.org/abs/2205.11342']
globuslabs/ScholarBERT_10,globuslabs,['arxiv.org/abs/2205.11342']
Geotrend/bert-base-en-bg-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Davlan/mT5_base_yoruba_adr,Davlan,['arxiv.org/abs/2003.10564']
iarfmoose/roberta-small-bulgarian,iarfmoose,['arxiv.org/abs/1907.11692']
w11wo/javanese-distilbert-small-imdb-classifier,w11wo,"['arxiv.org/abs/1910.01108', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
w11wo/javanese-bert-small-imdb,w11wo,"['arxiv.org/abs/1810.04805', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
mbeukman/xlm-roberta-base-finetuned-ner-kinyarwanda,mbeukman,['arxiv.org/abs/2103.11811']
MultiBertGunjanPatrick/multiberts-seed-1-400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/bengali_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-scicite,AdapterHub,['bibtex']
AdapterHub/roberta-base-pf-yelp_polarity,AdapterHub,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-1-0k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
fusing/ddim-lsun-bedroom,fusing,['arxiv.org/abs/2010.02502']
torchxrayvision/densenet121-res224-pc,torchxrayvision,"['arxiv.org/abs/2111.00595', 'bibtex']"
Splend1dchan/wav2vec2-large-100h-lv60-self,Splend1dchan,['arxiv.org/abs/2010.11430']
neuralmagic/oBERT-3-downstream-pruned-block4-90-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
torchxrayvision/densenet121-res224-mimic_ch,torchxrayvision,"['arxiv.org/abs/2111.00595', 'bibtex']"
neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
McGill-NLP/bart-qg-nq-checkpoint,McGill-NLP,['bibtex']
joaogante/test_text,joaogante,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
ctoraman/RoBERTa-TR-medium-word-28k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw-truncated-a7f080,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
madlag/bert-base-uncased-squad-v1-sparse0.25,madlag,['arxiv.org/abs/2005.07683']
Geotrend/distilbert-base-ja-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
McGill-NLP/bart-qg-mlquestions-selftraining,McGill-NLP,['bibtex']
espnet/kan-bayashi_libritts_tts_train_gst_xvector_trasnformer_raw_phn_tacotro-truncated-250027,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tftransformers/albert-base-v2,tftransformers,"['arxiv.org/abs/1909.11942', 'bibtex']"
espnet/kan-bayashi_vctk_gst_xvector_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Davlan/xlm-roberta-base-masakhaner,Davlan,"['arxiv.org/abs/2103.11811', {'title': 'Masakha{NER'}]"
espnet/su_openslr36,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-scitail,AdapterHub,['bibtex']
unicamp-dl/mt5-base-en-pt-msmarco-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
Geotrend/bert-base-en-ja-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-2-140k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa,Intel,['arxiv.org/abs/2111.05754']
Helsinki-NLP/opus-mt-tc-big-zle-fr,Helsinki-NLP,['bibtex']
sarnikowski/convbert-small-da-cased,sarnikowski,['arxiv.org/abs/2008.02496']
Helsinki-NLP/opus-mt-tc-big-en-et,Helsinki-NLP,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-1-1200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/GunnarThor_talromur_c_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_csj_asr_train_asr_conformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/wav2vec2-base-bg-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
espnet/kan-bayashi_libritts_gst_xvector_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
malteos/aspect-acl-scibert-scivocab-uncased,malteos,['arxiv.org/abs/2010.06395']
espnet/siddhana_fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_fine-truncated-ef9dab,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/wav2vec2-base-pt-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
MultiBertGunjanPatrick/multiberts-seed-15,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfitwte,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
neuralmagic/oBERT-3-downstream-pruned-unstructured-90-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
mchochowski/test-model,mchochowski,['arxiv.org/abs/1512.03385']
w11wo/javanese-roberta-small,w11wo,"['arxiv.org/abs/1907.11692', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
espnet/kan-bayashi_csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Davlan/mbart50-large-yor-eng-mt,Davlan,['arxiv.org/abs/2103.08647']
ufal/byt5-small-multilexnorm2021-sr,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
rmihaylov/roberta-base-use-qa-theseus-bg,rmihaylov,['arxiv.org/abs/2004.09813']
ilan541/sbert_ssid,ilan541,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
pyf98/slurp_entity_branchformer,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
questgen/paraphrase-multilingual-mpnet-base-v2-feature-extraction-pipeline,questgen,"['arxiv.org/abs/1908.10084', 'bibtex']"
AdapterHub/roberta-base-pf-mit_movie_trivia,AdapterHub,['bibtex']
AdapterHub/roberta-base-pf-copa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
AdapterHub/bert-base-uncased-pf-cq,AdapterHub,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-0-1900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/distilbert-base-en-ru-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
uva-irlab/quretec,uva-irlab,['arxiv.org/abs/2005.11723']
unicamp-dl/ptt5-base-pt-msmarco-10k-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
Geotrend/distilbert-base-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-25lang-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Multilingual BERT'}]
Geotrend/bert-base-en-ru-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
it5/it5-large-informal-to-formal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Geotrend/bert-base-en-fr-es-pt-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Multilingual BERT'}]
Geotrend/bert-base-en-ro-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
benjamin/roberta-base-wechsel-swahili,benjamin,['bibtex']
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_transformer_teacher_r-truncated-74c1b4,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/Shinji_Watanabe_spgispeech_asr_train_asr_conformer6_n_fft512_hop_lengt-truncated-a013d0,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_vctk_gst_conformer_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jac-truncated-6f4cf5,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
frgfm/cspdarknet53_mish,frgfm,"['arxiv.org/abs/1911.11929', 'bibtex']"
espnet/kan-bayashi_vctk_tts_train_xvector_conformer_fastspeech2_transformer_t-truncated-69a657,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_libritts_tts_train_xvector_trasnformer_raw_phn_tacotron_g2-truncated-e5fb13,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
allenai/aspire-biencoder-biomed-scib,allenai,['arxiv.org/abs/2111.08366']
espnet/roshansh_how2_asr_raw_ft_sum_valid.acc,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tftransformers/bert-large-cased,tftransformers,"['arxiv.org/abs/1810.04805', 'bibtex']"
joaogante/test_img,joaogante,"['arxiv.org/abs/2010.11929', {'title': 'Imagenet: A large-scale hierarchical image database'}]"
Geotrend/distilbert-base-en-ur-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_tsukuyomi_full_band_vits_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tau/splinter-large,tau,['bibtex']
AdapterHub/bert-base-uncased-pf-comqa,AdapterHub,['bibtex']
mrm8488/convbert-base-spanish,mrm8488,['arxiv.org/abs/2008.02496']
mbeukman/xlm-roberta-base-finetuned-ner-luo,mbeukman,['arxiv.org/abs/2103.11811']
AdapterHub/bert-base-uncased-pf-emo,AdapterHub,['bibtex']
khavitidala/finetuned-indobartv2-id-su,khavitidala,"['arxiv.org/abs/2104.08200', {'title': 'IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation'}]"
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-luganda,mbeukman,['arxiv.org/abs/2103.11811']
tftransformers/bert-large-uncased-whole-word-masking,tftransformers,"['arxiv.org/abs/1810.04805', 'bibtex']"
byeongal/gpt2-medium,byeongal,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
keras-io/denoising-diffusion-implicit-models,keras-io,['arxiv.org/abs/2010.02502']
AdapterHub/bert-base-uncased-pf-winogrande,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
Classroom-workshop/assignment1-francesco,Classroom-workshop,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
AdapterHub/bert-base-uncased-pf-yelp_polarity,AdapterHub,['bibtex']
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_acce-truncated-be0f66,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
bookbot/id-g2p-lstm,bookbot,['doi.org/10.1162/neco.1997.9.8.1735)']
mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-luo,mbeukman,['arxiv.org/abs/2103.11811']
bookbot/wav2vec2-xls-r-adult-child-cls,bookbot,['arxiv.org/abs/2111.09296']
keras-io/graph-attention-nets,keras-io,['arxiv.org/abs/1710.10903']
espnet/kan-bayashi_ljspeech_tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
eugenesiow/carn-bam,eugenesiow,"['arxiv.org/abs/1803.08664', {'title': 'Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network'}]"
it5/it5-base-repubblica-to-ilgiornale,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
nielsr/coref-roberta-large,nielsr,['arxiv.org/abs/2004.06870']
fusing/ddpm-lsun-church,fusing,['arxiv.org/abs/2006.11239']
it5/mt5-base-wiki-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Davlan/mt5_base_yor_eng_mt,Davlan,['arxiv.org/abs/2103.08647']
fusing/ddpm-celeba-hq-ema,fusing,['arxiv.org/abs/2006.11239']
AdapterHub/roberta-base-pf-trec,AdapterHub,['bibtex']
AdapterHub/bert-base-uncased-pf-imdb,AdapterHub,['bibtex']
AdapterHub/bert-base-uncased-pf-quoref,AdapterHub,['bibtex']
microsoft/unispeech-sat-base-sv,microsoft,['arxiv.org/abs/2110.05752']
NDugar/2epochv3mlni,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
ahmednasser/DistilBert-FakeNews,ahmednasser,['arxiv.org/abs/1910.01108']
DrishtiSharma/lwg_cartoon_faces,DrishtiSharma,['bibtex']
SaulLu/cotet5_small_fix,SaulLu,['arxiv.org/abs/2109.00859']
google/t5-efficient-small-ff9000,google,['arxiv.org/abs/2109.10686']
AdapterHub/bert-base-uncased-pf-duorc_s,AdapterHub,['bibtex']
yanaiela/roberta-base-epoch_82,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
yanaiela/roberta-base-epoch_0,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_14,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
Muennighoff/SGPT-125M-weightedmean-nli,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
yanaiela/roberta-base-epoch_81,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
m3hrdadfi/albert-fa-base-v2-clf-digimag,m3hrdadfi,[{'title': 'ParsBERT: Transformer-based Model for Persian Language Understanding'}]
google/t5-efficient-small-kv32,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-ff6000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-dm1000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-kv256,google,['arxiv.org/abs/2109.10686']
patrickvonplaten/data2vec-audio-base-960h-4-gram,patrickvonplaten,['arxiv.org/abs/2202.03555']
tftransformers/albert-xxlarge-v1,tftransformers,"['arxiv.org/abs/1909.11942', 'bibtex']"
jcblaise/electra-tagalog-small-uncased-generator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
AdapterHub/bert-base-uncased-pf-ud_deprel,AdapterHub,['bibtex']
google/t5-efficient-small-dm2000,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-ff12000,google,['arxiv.org/abs/2109.10686']
baffo32/gpt2-ptmap,baffo32,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
Classroom-workshop/assignment1-joane,Classroom-workshop,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
pyf98/librispeech_branchformer_e18_linear3072,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
yanaiela/roberta-base-epoch_12,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_55,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_53,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_47,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_50,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_41,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_49,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_5,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
espnet/Shinji_Watanabe_laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
yanaiela/roberta-base-epoch_33,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_57,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_58,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_68,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_61,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_60,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_13,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_18,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_43,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_9,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
sismetanin/mbart_ru_sum_gazeta-ru-sentiment-rusentiment,sismetanin,['bibtex']
HueyNemud/das22-10-camembert_pretrained,HueyNemud,['doi.org/10.1007/978-3-031-06555-2_30']
yanaiela/roberta-base-epoch_39,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_75,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_78,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
AdapterHub/roberta-base-pf-social_i_qa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
yanaiela/roberta-base-epoch_1,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_74,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_11,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_77,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_25,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_32,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_38,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_37,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_26,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
glasses/resnet34d,glasses,['arxiv.org/abs/1512.03385']
tftransformers/albert-xlarge-v1,tftransformers,"['arxiv.org/abs/1909.11942', 'bibtex']"
AdapterHub/bert-base-uncased-pf-wnut_17,AdapterHub,['bibtex']
Geotrend/bert-base-en-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-tr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
tftransformers/bart-base,tftransformers,"['arxiv.org/abs/1910.13461', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-5,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
NimaBoscarino/efficientformer-l1-300,NimaBoscarino,"['arxiv.org/abs/2206.01191', {'title': 'EfficientFormer: Vision Transformers at MobileNet Speed'}]"
blinoff/ru-gpt2-medium-rdf-2-text,blinoff,['bibtex']
AdapterHub/bert-base-uncased-pf-record,AdapterHub,['bibtex']
AdapterHub/roberta-base-pf-rotten_tomatoes,AdapterHub,['bibtex']
khavitidala/xlmroberta-large-fine-tuned-indo-hoax-classification,khavitidala,"['arxiv.org/abs/1911.02116', 'bibtex']"
flax-community/gpt-neo-1.3B-apps-all-2,flax-community,['arxiv.org/abs/2107.03374']
w11wo/javanese-roberta-small-imdb,w11wo,"['arxiv.org/abs/1907.11692', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
tals/albert-base-vitaminc,tals,['bibtex']
espnet/german_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
google/t5-efficient-tiny-nl6,google,['arxiv.org/abs/2109.10686']
kinit/slovakbert-pos,kinit,"['arxiv.org/abs/2109.15254', 'bibtex']"
swtx/ernie-gram-chinese,swtx,['arxiv.org/abs/2010.12148']
AdapterHub/roberta-base-pf-mrpc,AdapterHub,['bibtex']
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-igbo,mbeukman,['arxiv.org/abs/2103.11811']
it5/mt5-small-question-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
AdapterHub/bert-base-uncased-pf-commonsense_qa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
Geotrend/bert-base-bg-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba,mbeukman,['arxiv.org/abs/2103.11811']
AdapterHub/roberta-base-pf-conll2000,AdapterHub,['bibtex']
espnet/Chenda_Li_wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
yanaiela/roberta-base-epoch_48,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
nvidia/stt_en_citrinet_384_ls,nvidia,['arxiv.org/abs/2104.01721']
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf,CAMeL-Lab,"['arxiv.org/abs/2103.06678', 'bibtex']"
relbert/roberta-large-semeval2012-average-no-mask-prompt-a-loob-conceptnet-validated,relbert,['bibtex']
LiYuan/Amazon-Cross-Encoder-Classification,LiYuan,['doi.org/10.48550/arxiv.1908.10084']
facebook/wav2vec2-base-10k-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
rufimelo/Legal-BERTimbau-sts-large-v2,rufimelo,['bibtex']
microsoft/xclip-base-patch16-ucf-8-shot,microsoft,['arxiv.org/abs/2208.02816']
MultiBertGunjanPatrick/multiberts-seed-16,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
mbeukman/xlm-roberta-base-finetuned-ner-wolof,mbeukman,['arxiv.org/abs/2103.11811']
frgfm/resnet34,frgfm,"['arxiv.org/abs/1512.03385', 'bibtex']"
facebook/spar-paq-bm25-lexmodel-query-encoder,facebook,['arxiv.org/abs/2110.06918']
w11wo/wav2vec2-xls-r-300m-zh-HK-v2,w11wo,['arxiv.org/abs/2111.09296']
izumi-lab/electra-small-paper-japanese-fin-generator,izumi-lab,"['arxiv.org/abs/2003.10555', {'title': '金融文書を用いた事前学習言語モデルの構築と検証'}]"
globuslabs/ScholarBERT-XL,globuslabs,['arxiv.org/abs/2205.11342']
ccdv/lsg-barthez-4096,ccdv,[{'title': 'BARThez: a Skilled Pretrained French Sequence-to-Sequence Model'}]
nvidia/nemo-megatron-gpt-5B,nvidia,['arxiv.org/abs/2101.00027']
obss/mt5-base-3task-highlight-tquad2,obss,"['arxiv.org/abs/2111.06476', {'title': 'Automated question generation and question answering from Turkish texts using text-to-text transformers'}]"
MultiBertGunjanPatrick/multiberts-seed-1,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated,relbert,['bibtex']
Hate-speech-CNERG/kannada-codemixed-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
arijitx/IndicBART-bn-QuestionGeneration,arijitx,[{'title': 'IndicBART: A Pre-trained Model for Natural Language Generation of Indic Languages'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob-conceptnet-validated,relbert,['bibtex']
DCU-NLP/electra-base-irish-cased-generator-v1,DCU-NLP,"['arxiv.org/abs/2107.12930', 'bibtex']"
rufimelo/Legal-BERTimbau-sts-large-ma-v2,rufimelo,['bibtex']
rmihaylov/bert-base-nli-theseus-bg,rmihaylov,['arxiv.org/abs/1810.04805']
ITESM/st_demo_2,ITESM,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
espnet/kan-bayashi_csmsc_vits,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-2000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Davlan/mt5_base_eng_yor_mt,Davlan,['arxiv.org/abs/2103.08647']
Classroom-workshop/assignment1-jack,Classroom-workshop,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
espnet/kan-bayashi_csmsc_fastspeech,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
NDugar/v2xl-again-mnli,NDugar,"['arxiv.org/abs/2006.03654', {'title': 'DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION'}]"
Geotrend/bert-base-lt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Classroom-workshop/assignment1-maria,Classroom-workshop,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
Geotrend/bert-base-en-fr-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
keras-io/structured-data-classification-grn-vsn,keras-io,['arxiv.org/abs/1912.09363']
relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob-conceptnet-validated,relbert,['bibtex']
espnet/kan-bayashi_libritts_tts_train_gst_xvector_conformer_fastspeech2_trans-truncated-c3209b,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
praf-choub/bart-mofe-rl-xsum,praf-choub,"['arxiv.org/abs/2110.07166', 'bibtex']"
RUCAIBox/mtl-summarization,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
espnet/siddhana_fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kamo-naoyuki_wsj,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
obss/mt5-small-3task-both-tquad2,obss,"['arxiv.org/abs/2111.06476', {'title': 'Automated question generation and question answering from Turkish texts using text-to-text transformers'}]"
facebook/wav2vec2-base-hu-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
ufal/byt5-small-multilexnorm2021-it,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
nvidia/stt_kab_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
espnet/GunnarThor_talromur_f_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
yanaiela/roberta-base-epoch_8,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
microsoft/xclip-base-patch16-kinetics-600-16-frames,microsoft,['arxiv.org/abs/2208.02816']
anlausch/aq_bert_ibm,anlausch,['bibtex']
Yaxin/roberta-large-ernie2-skep-en,Yaxin,[{'title': 'SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis'}]
yanaiela/roberta-base-epoch_44,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
espnet/americasnlp22-asr-bzd,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
yanaiela/roberta-base-epoch_45,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-hausa,mbeukman,['arxiv.org/abs/2103.11811']
yanaiela/roberta-base-epoch_42,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_7,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
NimaBoscarino/aot-gan-celebahq,NimaBoscarino,['bibtex']
Maltehb/aelaectra-danish-electra-small-uncased,Maltehb,['arxiv.org/abs/2003.10555']
AdapterHub/roberta-base-pf-ud_pos,AdapterHub,['bibtex']
espnet/siddhana_fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_r-truncated-36174d,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/wav2vec2-base-nl-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
gaetangate/bart-large_genrl_simpleq,gaetangate,"['arxiv.org/abs/2108.07337', {'title': 'Generative relation linking for question answering over knowledge bases'}]"
Classroom-workshop/assignment1-jane,Classroom-workshop,"['arxiv.org/abs/2010.05171', {'title': 'fairseq S2T: Fast Speech-to-Text Modeling with fairseq'}]"
Geotrend/distilbert-base-en-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
MultiBertGunjanPatrick/multiberts-seed-1-300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
unicamp-dl/ptt5-base-pt-msmarco-100k-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ufal/byt5-small-multilexnorm2021-sl,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
rmihaylov/roberta-base-nli-stsb-bg,rmihaylov,['arxiv.org/abs/2004.09813']
clip-italian/clip-italian-final,clip-italian,['arxiv.org/abs/2103.00020']
tner/deberta-v3-large-mit-restaurant,tner,['bibtex']
espnet/YushiUeda_librimix_diar_enh_2_3_spk,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
bigscience/bloom-1b7-intermediate,bigscience,['arxiv.org/abs/1909.08053']
Muennighoff/SGPT-1.3B-weightedmean-nli,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
mbeukman/xlm-roberta-base-finetuned-ner-hausa,mbeukman,['arxiv.org/abs/2103.11811']
espnet/kamo-naoyuki_reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdata-truncated-0e9753,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-record,AdapterHub,['bibtex']
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
danjohnvelasco/roberta-tagalog-base-cohfie-v1,danjohnvelasco,"['arxiv.org/abs/2204.03251', 'doi.org/10.48550/arxiv.2204.03251,']"
frgfm/repvgg_a2,frgfm,"['arxiv.org/abs/2101.03697', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw-truncated-15ef5f,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ctoraman/RoBERTa-TR-medium-word-66k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
AdapterHub/roberta-base-pf-duorc_p,AdapterHub,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-8,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-120k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
w11wo/javanese-distilbert-small-imdb,w11wo,"['arxiv.org/abs/1910.01108', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
MultiBertGunjanPatrick/multiberts-seed-1-1700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
julien-c/kan-bayashi-jsut_tts_train_tacotron2,julien-c,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-10,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
yanaiela/roberta-base-epoch_67,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
unicamp-dl/ptt5-base-pt-msmarco-100k-v2,unicamp-dl,['arxiv.org/abs/2108.13897']
MultiBertGunjanPatrick/multiberts-seed-4-1900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
tftransformers/bert-large-cased-whole-word-masking,tftransformers,"['arxiv.org/abs/1810.04805', 'bibtex']"
pyronear/resnet18,pyronear,"['arxiv.org/abs/1512.03385', 'bibtex']"
AdapterHub/bert-base-uncased-pf-swag,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
AdapterHub/bert-base-uncased-pf-art,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
MultiBertGunjanPatrick/multiberts-seed-2-60k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
tadejmagajna/flair-sl-pos,tadejmagajna,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
MultiBertGunjanPatrick/multiberts-seed-3-2000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-1100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
ctoraman/RoBERTa-TR-medium-word-7k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ctoraman/RoBERTa-TR-medium-morph-7k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ctoraman/RoBERTa-TR-medium-bpe-28k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ctoraman/RoBERTa-TR-medium-bpe-7k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ctoraman/RoBERTa-TR-medium-bpe-16k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
espnet/ftshijt_mls_asr_transformer_valid.acc.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-1700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
w11wo/javanese-distilbert-small,w11wo,"['arxiv.org/abs/1910.01108', {'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]"
espnet/kan-bayashi_jsut_transformer_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_schedule-truncated-c8e5f9,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-1300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/distilbert-base-en-tr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-3-100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
TheRensselaerIDEA/gpt2-large-covid-tweet-response,TheRensselaerIDEA,['arxiv.org/abs/2204.04353']
AdapterHub/roberta-base-pf-winogrande,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
AdapterHub/roberta-base-pf-hellaswag,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
espnet/kan-bayashi_jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/accented_french_openslr57_ASR_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-1500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-1200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
abhi1nandy2/EManuals_BERT,abhi1nandy2,['bibtex']
Geotrend/bert-base-de-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-0-1200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
AdapterHub/bert-base-uncased-pf-duorc_p,AdapterHub,['bibtex']
ncthuan/vi-distilled-msmarco-MiniLM-L12-cos-v5,ncthuan,"['arxiv.org/abs/2004.09813', 'bibtex']"
AdapterHub/roberta-base-pf-quartz,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
MultiBertGunjanPatrick/multiberts-seed-6,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
AdapterHub/roberta-base-pf-swag,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-97,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
espnet/GunnarThor_talromur_e_tacotron2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
keras-io/Node2Vec_MovieLens,keras-io,['arxiv.org/abs/1607.00653']
glasses/eca_resnet26t,glasses,['arxiv.org/abs/1512.03385']
SaulLu/test-model,SaulLu,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-2-160k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
qarib/bert-base-qarib_far_6500k,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
neuralmagic/oBERT-6-downstream-dense-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
tftransformers/albert-xlarge-v2,tftransformers,"['arxiv.org/abs/1909.11942', 'bibtex']"
tftransformers/albert-xxlarge-v2,tftransformers,"['arxiv.org/abs/1909.11942', 'bibtex']"
Geotrend/distilbert-base-en-bg-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/GunnarThor_talromur_e_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-0k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Muennighoff/SBERT-base-nli-v2-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
DataikuNLP/paraphrase-albert-small-v2,DataikuNLP,"['arxiv.org/abs/1908.10084', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-1900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
AkshaySg/langid,AkshaySg,[{'title': '{VoxLingua107'}]
MultiBertGunjanPatrick/multiberts-seed-0-20k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Muennighoff/SGPT-125M-mean-nli-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/distilbert-base-no-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
neuralmagic/oBERT-12-downstream-pruned-block4-80-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
mrm8488/squeezebert-finetuned-squadv1,mrm8488,['arxiv.org/abs/2006.11316']
arredondos/my_sentence_transformer,arredondos,"['arxiv.org/abs/1904.06472', 'doi.org/10.1145/2623330.2623677)']"
benjamin/gpt2-wechsel-sundanese,benjamin,['bibtex']
flax-community/bigband,flax-community,['arxiv.org/abs/2007.14062']
espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_wav2vec2_2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-1600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
AdapterHub/roberta-base-pf-cosmos_qa,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
LeBenchmark/wav2vec-FR-1K-Female-large,LeBenchmark,"['arxiv.org/abs/2204.01397', {'title': 'A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems'}]"
Geotrend/distilbert-base-en-fr-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
Geotrend/bert-base-en-pl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
AdapterHub/bert-base-uncased-pf-sick,AdapterHub,['bibtex']
Geotrend/bert-base-en-es-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
johnslegers/stable-diffusion-v1-4,johnslegers,['arxiv.org/abs/2207.12598']
ctoraman/RoBERTa-TR-medium-bpe-44k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
Krystalan/mdialbart_de,Krystalan,['arxiv.org/abs/2202.05599']
keras-io/bit,keras-io,['arxiv.org/abs/1912.11370']
Geotrend/distilbert-base-th-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
hfl/chinese-electra-small-ex-generator,hfl,"['arxiv.org/abs/2004.13922', 'bibtex']"
espnet/kan-bayashi_vctk_gst_xvector_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tner/bert-base-tweetner7-random,tner,['bibtex']
mbeukman/xlm-roberta-base-finetuned-naija-finetuned-ner-naija,mbeukman,['arxiv.org/abs/2103.11811']
DataikuNLP/distiluse-base-multilingual-cased-v1,DataikuNLP,"['arxiv.org/abs/1908.10084', 'bibtex']"
pyf98/slurp_entity_conformer,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/bert-base-uncased-pf-newsqa,AdapterHub,['bibtex']
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic,mbeukman,['arxiv.org/abs/2103.11811']
AdapterHub/roberta-base-pf-quail,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_hubert,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-80k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
rmihaylov/gpt2-small-theseus-bg,rmihaylov,['arxiv.org/abs/2002.02925']
AdapterHub/roberta-base-pf-multirc,AdapterHub,['bibtex']
dbmdz/flair-clef-hipe-german-base,dbmdz,['arxiv.org/abs/2011.06993']
Geotrend/bert-base-en-ur-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
AdapterHub/roberta-base-pf-emotion,AdapterHub,['bibtex']
jkang/espnet2_mini_librispeech_diar,jkang,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Leostronkest/DialoGPT,Leostronkest,['arxiv.org/abs/1911.00536']
AdapterHub/roberta-base-pf-pmb_sem_tagging,AdapterHub,['bibtex']
mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
Geotrend/distilbert-base-sw-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
ctoraman/RoBERTa-TR-medium-wp-16k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
rmihaylov/roberta-base-nli-stsb-theseus-bg,rmihaylov,['arxiv.org/abs/2004.09813']
uer/chinese_roberta_L-10_H-128,uer,"['arxiv.org/abs/1909.05658', {'title': 'Bert: Pre-training of deep bidirectional transformers for language understanding'}]"
AdapterHub/roberta-base-pf-emo,AdapterHub,['bibtex']
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-kinyarwanda,mbeukman,['arxiv.org/abs/2103.11811']
it5/mt5-base-headline-generation,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
MultiBertGunjanPatrick/multiberts-seed-4-1200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
keras-io/semantic-image-clustering,keras-io,['arxiv.org/abs/2005.12320']
cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020,cardiffnlp,['bibtex']
AdapterHub/bert-base-uncased-pf-squad,AdapterHub,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-4-900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
it5/it5-efficient-small-el32-informal-to-formal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
google/t5-efficient-xl-nl6,google,['arxiv.org/abs/2109.10686']
yanaiela/roberta-base-epoch_10,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
Vikasbhandari/wav2vec2-train,Vikasbhandari,['arxiv.org/abs/2010.11430']
AdapterHub/roberta-base-pf-scitail,AdapterHub,['bibtex']
frgfm/rexnet1_3x,frgfm,"['arxiv.org/abs/2007.00992', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-140k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
yanaiela/roberta-base-epoch_19,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
frgfm/cspdarknet53,frgfm,"['arxiv.org/abs/1911.11929', 'bibtex']"
yanaiela/roberta-base-epoch_17,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_15,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_2,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_3,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
gokceuludogan/WarmMolGenOne,gokceuludogan,"['doi.org/10.1093/bioinformatics/btac482}', 'bibtex']"
Geotrend/bert-base-en-lt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-4-1000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
gokceuludogan/WarmMolGenTwo,gokceuludogan,"['doi.org/10.1093/bioinformatics/btac482}', 'bibtex']"
yanaiela/roberta-base-epoch_4,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
Geotrend/bert-base-en-fr-nl-ru-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-en-fr-zh-ja-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
classla/bcms-bertic-generator,classla,['bibtex']
yanaiela/roberta-base-epoch_34,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
google/t5-efficient-small-dl8,google,['arxiv.org/abs/2109.10686']
frgfm/rexnet2_0x,frgfm,"['arxiv.org/abs/2007.00992', 'bibtex']"
RamAnanth1/decision-transformers-hopper-expert,RamAnanth1,['arxiv.org/abs/2106.01345']
Muennighoff/SGPT-2.7B-weightedmean-nli-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
ELiRF/mbart-large-cc25-dacsa-ca,ELiRF,"['arxiv.org/abs/2001.08210', 'bibtex']"
tftransformers/mt5-base,tftransformers,['arxiv.org/abs/2010.11934']
yanaiela/roberta-base-epoch_22,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
tals/albert-base-vitaminc_flagging,tals,['bibtex']
yanaiela/roberta-base-epoch_23,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_21,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
pyronear/mobilenet_v3_small,pyronear,"['arxiv.org/abs/1905.02244', 'bibtex']"
yanaiela/roberta-base-epoch_29,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_62,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
google/t5-efficient-base-el6,google,['arxiv.org/abs/2109.10686']
google/t5-efficient-small-kv16,google,['arxiv.org/abs/2109.10686']
Geotrend/bert-base-da-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
yanaiela/roberta-base-epoch_52,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_54,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
byeongal/bert-base-uncased,byeongal,"['arxiv.org/abs/1810.04805', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
microsoft/wavlm-base-sd,microsoft,['arxiv.org/abs/2110.13900']
bayartsogt/tts_transformer-mn-mbspeech,bayartsogt,['arxiv.org/abs/1809.08895']
neuralmagic/oBERT-3-upstream-pretrained-dense,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
ai4bharat/MultiIndicQuestionGenerationUnified,ai4bharat,"['arxiv.org/abs/2203.05437', {'title': 'IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages'}]"
espnet/Karthik_DSTC2_asr_train_asr_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/akreal_swbd_da_hubert_conformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-80k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw-truncated-569e81,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Maltehb/aelaectra-danish-electra-small-uncased-ner-dane,Maltehb,['arxiv.org/abs/2003.10555']
Finnish-NLP/byt5-base-finnish,Finnish-NLP,['arxiv.org/abs/2105.13626']
ctoraman/RoBERTa-TR-medium-morph-66k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ctoraman/RoBERTa-TR-medium-wp-28k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
IDEA-CCNL/Erlangshen-ZEN2-345M-Chinese,IDEA-CCNL,"['arxiv.org/abs/2105.01279', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili,mbeukman,['arxiv.org/abs/2103.11811']
bookbot/distil-wav2vec2-adult-child-cls-52m,bookbot,['arxiv.org/abs/2006.11477']
Geotrend/bert-base-en-el-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
RVN/XLMR-MaCoCu-is,RVN,['bibtex']
philschmid/finbert-pretrain-yiyanghkust,philschmid,['arxiv.org/abs/2006.08097']
asapp/sew-d-mid-100k,asapp,['arxiv.org/abs/2109.06870']
benyong/testmodel,benyong,"['arxiv.org/abs/1810.04805', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-80k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-12-upstream-pretrained-dense,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
MultiBertGunjanPatrick/multiberts-seed-4-1400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-1600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-1800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
mbeukman/xlm-roberta-base-finetuned-ner-igbo,mbeukman,['arxiv.org/abs/2103.11811']
MultiBertGunjanPatrick/multiberts-seed-0-60k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-1400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
obss/mt5-base-3task-highlight-combined3,obss,"['arxiv.org/abs/2111.06476', {'title': 'Automated question generation and question answering from Turkish texts using text-to-text transformers'}]"
probing-vits/vit_b16_patch16_224_i1k,probing-vits,['arxiv.org/abs/2010.11929']
Muennighoff/SGPT-125M-lasttoken-msmarco-specb,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
probing-vits/vit_b16_patch16_224_i21k_i1k,probing-vits,['arxiv.org/abs/2010.11929']
neuralmagic/oBERT-12-downstream-dense-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
Muennighoff/SGPT-125M-mean-nli-linearthenpool5,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-portuguese,Edresson,['arxiv.org/abs/2204.00618']
facebook/wav2vec2-base-it-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
allenai/tk-instruct-3b-def-pos-neg-expl,allenai,"['arxiv.org/abs/1910.10683', {'title': 'Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks'}]"
neuralmagic/oBERT-teacher-mnli,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-downstream-pruned-unstructured-90-mnli,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
Muennighoff/SGPT-125M-lasttoken-nli,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
Muennighoff/SGPT-125M-scratchmean-nli,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
Muennighoff/SGPT-125M-mean-nli-linear5,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
RUCAIBox/mtl-question-answering,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
Muennighoff/SBERT-base-msmarco,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
Muennighoff/SBERT-base-msmarco-bitfit,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
it5/mt5-small-informal-to-formal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
tner/deberta-v3-large-wnut2017,tner,['bibtex']
AdapterHub/roberta-base-pf-duorc_s,AdapterHub,['bibtex']
espnet/kan-bayashi_jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_a-truncated-d7d5d0,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
popcornell/clarity21_train_enh_beamformer_mvdr,popcornell,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
madlag/bert-base-uncased-squad1.1-block-sparse-0.07-v1,madlag,['arxiv.org/abs/2005.07683']
Geotrend/bert-base-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
tugstugi/bert-large-mongolian-cased,tugstugi,['arxiv.org/abs/1810.04805']
facebook/wav2vec2-base-sk-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
mbeukman/xlm-roberta-base-finetuned-ner-amharic,mbeukman,['arxiv.org/abs/2103.11811']
MultiBertGunjanPatrick/multiberts-seed-1-60k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
facebook/wav2vec2-base-da-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
espnet/ml_openslr63,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend-truncated-55c091,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend-truncated-b76af5,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/Chenda_Li_wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Muennighoff/SGPT-125M-learntmean-nli,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
MultiBertGunjanPatrick/multiberts-seed-4-600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/kamo-naoyuki_reverb_asr_train_asr_transformer4_raw_char_batch_bins1600-truncated-1b72bb,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-60k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
facebook/wav2vec2-base-pl-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
MultiBertGunjanPatrick/multiberts-seed-4-500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
obss/mt5-small-3task-prepend-tquad2,obss,"['arxiv.org/abs/2111.06476', {'title': 'Automated question generation and question answering from Turkish texts using text-to-text transformers'}]"
jcblaise/electra-tagalog-small-cased-generator,jcblaise,[{'title': 'Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets'}]
Helsinki-NLP/opus-mt-tc-big-zle-pt,Helsinki-NLP,['bibtex']
aajrami/bert-mlm-base,aajrami,"['arxiv.org/abs/2203.10415', {'title': 'How does the pre-training objective affect what large language models learn about linguistic properties?'}]"
espnet/kamo-naoyuki_dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scp-truncated-2fd1f8,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/americasnlp22-asr-gn,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Muennighoff/SBERT-large-nli-v2,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
Geotrend/distilbert-base-hi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
w11wo/javanese-gpt2-small,w11wo,[{'title': 'Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures'}]
reichenbach/switch-transformer-classification,reichenbach,['arxiv.org/abs/2101.03961']
johnowhitaker/lwg_colorbs,johnowhitaker,['bibtex']
iuliaturc/bert_uncased_L-2_H-128_A-2,iuliaturc,"['arxiv.org/abs/1908.08962', {'title': 'Well-Read Students Learn Better: On the Importance of Pre-training Compact Models'}]"
LanceaKing/spkrec-ecapa-cnceleb,LanceaKing,['bibtex']
TweebankNLP/bertweet-tb2-pos-tagging,TweebankNLP,[{'title': 'Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis'}]
bookbot/wav2vec2-xls-r-adult-child-id-cls,bookbot,['arxiv.org/abs/2111.09296']
ufal/byt5-small-multilexnorm2021-es,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
osanseviero/my-new-sentence-transformer,osanseviero,"['arxiv.org/abs/1908.10084', 'bibtex']"
yanaiela/roberta-base-epoch_28,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_27,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_16,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_40,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_24,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
tftransformers/gpt2-large,tftransformers,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
obss/mt5-small-3task-highlight-tquad2,obss,"['arxiv.org/abs/2111.06476', {'title': 'Automated question generation and question answering from Turkish texts using text-to-text transformers'}]"
tau/t5-v1_1-base-sled,tau,"['arxiv.org/abs/2208.00748', {'title': 'Efficient Long-Text Understanding with Short-Text Models'}]"
yanaiela/roberta-base-epoch_30,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_36,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_35,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_65,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_46,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_51,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_59,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_69,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_56,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_64,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_66,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_72,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_20,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
bookbot/distil-wav2vec2-adult-child-id-cls-52m,bookbot,['arxiv.org/abs/2006.11477']
yanaiela/roberta-base-epoch_71,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_6,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
yanaiela/roberta-base-epoch_73,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
UBC-NLP/prags2,UBC-NLP,['bibtex']
Geotrend/bert-base-ur-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese,Edresson,['arxiv.org/abs/2204.00618']
espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
YushiUeda/callhome_adapt_simu,YushiUeda,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
aajrami/bert-ascii-base,aajrami,"['arxiv.org/abs/2203.10415', {'title': 'How does the pre-training objective affect what large language models learn about linguistic properties?'}]"
Classroom-workshop/assignment1-omar,Classroom-workshop,['arxiv.org/abs/2006.11477']
AdapterHub/roberta-base-pf-wic,AdapterHub,['bibtex']
it5/mt5-base-ilgiornale-to-repubblica,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
nielsr/tapex-large-finetuned-tabfact,nielsr,['arxiv.org/abs/2107.07653']
DataikuNLP/paraphrase-MiniLM-L6-v2,DataikuNLP,"['arxiv.org/abs/1908.10084', 'bibtex']"
GroNLP/bert-base-dutch-cased-gronings,GroNLP,['arxiv.org/abs/2105.02855']
Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian,Edresson,['arxiv.org/abs/2204.00618']
yanaiela/roberta-base-epoch_63,yanaiela,"['arxiv.org/abs/1907.11692', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-1800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-1600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
lgris/bp500-base10k_voxpopuli,lgris,['arxiv.org/abs/2012.03411']
espnet/Karthik_sinhala_asr_train_asr_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
rmihaylov/bert-base-ner-theseus-bg,rmihaylov,['arxiv.org/abs/1810.04805']
Geotrend/distilbert-base-el-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
mohadz/arabert_arabic_covid19,mohadz,['arxiv.org/abs/2004.04315']
MultiBertGunjanPatrick/multiberts-seed-1-160k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
huggan/lwg_aurora,huggan,['bibtex']
sw005320/aidatatang_200zh_conformer,sw005320,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-140k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Hate-speech-CNERG/urdu-codemixed-abusive-MuRIL,Hate-speech-CNERG,"['arxiv.org/abs/2204.12543', {'title': 'Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages'}]"
Helsinki-NLP/opus-mt-tc-big-fr-zle,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-cel-en,Helsinki-NLP,['bibtex']
RUCAIBox/mtl-task-dialog,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
mbeukman/xlm-roberta-base-finetuned-luganda-finetuned-ner-luganda,mbeukman,['arxiv.org/abs/2103.11811']
vneralla/xlrs-53-finnish,vneralla,['arxiv.org/abs/2006.13979']
aheba31/test-predictor,aheba31,['bibtex']
nvidia/stt_hr_conformer_transducer_large,nvidia,['arxiv.org/abs/2005.08100']
it5/it5-efficient-small-el32-repubblica-to-ilgiornale,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Geotrend/bert-base-en-zh-hi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-ur-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
it5/it5-efficient-small-el32-formal-to-informal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
AdapterHub/roberta-base-pf-newsqa,AdapterHub,['bibtex']
Geotrend/bert-base-en-fr-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
ufal/byt5-small-multilexnorm2021-hr,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
GroNLP/bert-base-dutch-cased-upos-alpino-frisian,GroNLP,['arxiv.org/abs/2105.02855']
it5/mt5-base-repubblica-to-ilgiornale,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
it5/it5-small-ilgiornale-to-repubblica,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Geotrend/distilbert-base-en-es-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
DataikuNLP/camembert-base,DataikuNLP,"['arxiv.org/abs/1911.03894', {'title': 'CamemBERT: a Tasty French Language Model'}]"
relbert/roberta-large-semeval2012-average-no-mask-prompt-c-triplet,relbert,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-0,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
eugenesiow/mdsr-bam,eugenesiow,"['arxiv.org/abs/1707.02921', {'title': 'Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network'}]"
mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic,mbeukman,['arxiv.org/abs/2103.11811']
it5/it5-large-ilgiornale-to-repubblica,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Geotrend/distilbert-base-en-es-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
keras-io/pointnet_segmentation,keras-io,['arxiv.org/abs/1612.00593']
it5/mt5-small-wiki-summarization,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
it5/mt5-small-question-answering,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
nvidia/stt_hr_conformer_ctc_large,nvidia,['arxiv.org/abs/2005.08100']
it5/mt5-small-formal-to-informal,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
asapp/sew-d-mid-k127-400k,asapp,['arxiv.org/abs/2109.06870']
rmihaylov/roberta-base-use-qa-bg,rmihaylov,['arxiv.org/abs/2004.09813']
LeBenchmark/wav2vec-FR-1K-Male-large,LeBenchmark,"['arxiv.org/abs/2204.01397', {'title': 'A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems'}]"
Geotrend/distilbert-base-en-fr-de-no-da-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
facebook/spar-marco-bm25-lexmodel-query-encoder,facebook,['arxiv.org/abs/2110.06918']
nielsr/tapex-large-finetuned-wikisql,nielsr,['arxiv.org/abs/2107.07653']
cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all,cardiffnlp,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce-classification-conceptnet-validated,relbert,['bibtex']
espnet/Yen-Ju_Lu_spatilaizedslurp_asr_train_asr_conformer_transformer_valid.acc.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
nvidia/nemo-megatron-gpt-20B,nvidia,['arxiv.org/abs/2101.00027']
cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all,cardiffnlp,['bibtex']
cardiffnlp/roberta-large-tweet-topic-multi-2020,cardiffnlp,['bibtex']
nielsr/tapex-large-finetuned-sqa,nielsr,['arxiv.org/abs/2107.07653']
keras-io/timeseries-classification-from-scratch,keras-io,['arxiv.org/abs/1611.06455']
dragonSwing/viwav2vec2-base-1.5k,dragonSwing,['arxiv.org/abs/2006.11477']
MultiBertGunjanPatrick/multiberts-seed-2-2000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
tner/deberta-v3-large-bionlp2004,tner,['bibtex']
espnet/kan-bayashi_jsut_fastspeech2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
RamAnanth1/decision-transformers-walker2d-expert,RamAnanth1,['arxiv.org/abs/2106.01345']
spencer/contriever_pipeline,spencer,['arxiv.org/abs/2112.09118']
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
Geotrend/bert-base-no-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/fsc_challenge_slu_2pass_conformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/bert-base-en-el-ru-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
ctoraman/RoBERTa-TR-medium-morph-28k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ctoraman/RoBERTa-TR-medium-wp-7k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
ufal/byt5-small-multilexnorm2021-da,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
espnet/Shinji_Watanabe_librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
AdapterHub/roberta-base-pf-comqa,AdapterHub,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-2-1300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/anogkongda-librimix_enh_train_raw_valid.si_snr.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Goud/DziriBERT-summarization-goud,Goud,[{'title': 'Goud.ma: a News Article Dataset for Summarization in Moroccan Darija'}]
ctoraman/RoBERTa-TR-medium-bpe-66k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
MCG-NJU/videomae-base-short-finetuned-kinetics,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
MultiBertGunjanPatrick/multiberts-seed-22,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
AdapterHub/roberta-base-pf-art,AdapterHub,"['arxiv.org/abs/2104.08247', {'title': 'What to Pre-Train on? Efficient Intermediate Task Selection'}]"
relbert/roberta-large-semeval2012-average-no-mask-prompt-b-triplet,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-d-nce-classification-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-e-nce,relbert,['bibtex']
facebook/wav2vec2-base-lt-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
binaya-s/xls-r-300m-en,binaya-s,['arxiv.org/abs/2006.11477']
facebook/wav2vec2-base-it-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-d-nce,relbert,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-0-40k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
l3cube-pune/hindi-marathi-dev-albert,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
l3cube-pune/hindi-marathi-dev-bert,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
dibsondivya/ernie-phmtweets-sutd,dibsondivya,[{'title': 'ERNIE 2.0: A Continual Pre-training Framework for Language Understanding'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification-conceptnet-validated,relbert,['bibtex']
l3cube-pune/hindi-roberta,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-c-nce,relbert,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-3-120k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-b-nce,relbert,['bibtex']
AdapterHub/roberta-base-pf-conll2003_pos,AdapterHub,['bibtex']
Muennighoff/SGPT-125M-weightedmean-nli-bitfit-linearthenpool1-noact,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
relbert/roberta-large-semeval2012-v2-average-no-mask-prompt-a-nce,relbert,['bibtex']
Geotrend/distilbert-base-en-uk-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-0-2000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-12-downstream-pruned-unstructured-80-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
MultiBertGunjanPatrick/multiberts-seed-1-2000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
jdang/dummy-model,jdang,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
Geotrend/bert-base-en-no-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
facebook/wav2vec2-large-nl-voxpopuli,facebook,['arxiv.org/abs/2101.00390']
MultiBertGunjanPatrick/multiberts-seed-0-700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Davlan/byt5-base-yor-eng-mt,Davlan,['arxiv.org/abs/2103.08647']
relbert/roberta-large-semeval2012-average-no-mask-prompt-d-triplet,relbert,['bibtex']
Helsinki-NLP/opus-mt-tc-big-de-zle,Helsinki-NLP,['bibtex']
Davlan/mbart50-large-eng-yor-mt,Davlan,['arxiv.org/abs/2103.08647']
relbert/roberta-large-semeval2012-average-no-mask-prompt-e-triplet,relbert,['bibtex']
rmihaylov/bert-base-theseus-bg,rmihaylov,['arxiv.org/abs/1810.04805']
unicamp-dl/mt5-base-en-msmarco,unicamp-dl,['arxiv.org/abs/2108.13897']
Geotrend/distilbert-base-en-ja-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet,relbert,['bibtex']
Geotrend/distilbert-base-en-no-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-en-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-en-es-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce-classification-conceptnet-validated,relbert,['bibtex']
espnet/brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/distilbert-base-en-fr-es-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
it5/it5-efficient-small-el32-ilgiornale-to-repubblica,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Geotrend/distilbert-base-en-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-a-nce-conceptnet-validated,relbert,['bibtex']
Geotrend/distilbert-base-tr-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-conceptnet-validated,relbert,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-1-1600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
relbert/roberta-large-semeval2012-average-no-mask-prompt-d-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-c-nce-conceptnet-validated,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-b-nce-conceptnet-validated,relbert,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-4-0k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
fusing/ddpm-cifar10,fusing,['arxiv.org/abs/2006.11239']
neuralmagic/oBERT-6-upstream-pretrained-dense,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
nvidia/stt_en_citrinet_512_ls,nvidia,['arxiv.org/abs/2104.01721']
Geotrend/distilbert-base-en-es-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
DrishtiSharma/lwg_pokemon,DrishtiSharma,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-a-loob,relbert,['bibtex']
SaulLu/clip-vit-base-patch32,SaulLu,['arxiv.org/abs/2103.00020']
MultiBertGunjanPatrick/multiberts-seed-19,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
tftransformers/mt5-small,tftransformers,['arxiv.org/abs/2010.11934']
facebook/wav2vec2-large-north_germanic-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob,relbert,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob,relbert,['bibtex']
Helsinki-NLP/opus-mt-tc-big-zle-it,Helsinki-NLP,['bibtex']
relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob,relbert,['bibtex']
relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce,relbert,['bibtex']
relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce,relbert,['bibtex']
facebook/wav2vec2-base-sv-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
ctoraman/RoBERTa-TR-medium-word-16k,ctoraman,"['arxiv.org/abs/2204.08832', 'doi.org/10.48550/arxiv.2204.08832,']"
Geotrend/distilbert-base-en-fr-de-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-ro-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
relbert/roberta-large-semeval2012-average-no-mask-prompt-a-nce-classification-conceptnet-validated,relbert,['bibtex']
MCG-NJU/videomae-base-short-finetuned-ssv2,MCG-NJU,"['arxiv.org/abs/2203.12602', 'doi.org/10.48550/arxiv.2203.12602,']"
facebook/wav2vec2-base-cs-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
malteos/aspect-cord19-scibert-scivocab-uncased,malteos,['arxiv.org/abs/2010.06395']
superb/hubert-large-superb-ic,superb,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
Geotrend/bert-base-nl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
vaishnavi/indic-bert-512,vaishnavi,"[{'title': '{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages'}]"
dibsondivya/distilbert-phmtweets-sutd,dibsondivya,"[{'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
lgris/distilxlsr_bp_8-12,lgris,['arxiv.org/abs/2110.01900']
facebook/wav2vec2-base-ro-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
facebook/wav2vec2-large-romance-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
patrickvonplaten/data2vec-audio-base-100h-4-gram,patrickvonplaten,['arxiv.org/abs/2202.03555']
Yulinfeng/wsj0_2mix_enh_train_enh_dan_tf_raw_valid.si_snr.ave,Yulinfeng,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
it5/it5-small-repubblica-to-ilgiornale,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
sebastian-hofstaetter/uni-colberter-128-1-msmarco,sebastian-hofstaetter,"['arxiv.org/abs/2203.13088', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
fusing/ddpm-lsun-cat,fusing,['arxiv.org/abs/2006.11239']
Geotrend/distilbert-base-en-es-pt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
it5/it5-large-repubblica-to-ilgiornale,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Helsinki-NLP/opus-mt-tc-big-zle-gmq,Helsinki-NLP,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-0-900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-1000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
tner/bert-base-tweetner7-2020,tner,['bibtex']
Helsinki-NLP/opus-mt-tc-base-ces_slk-uk,Helsinki-NLP,['bibtex']
phjhk/hklegal-xlm-r-large-t,phjhk,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
rmihaylov/bert-base-bg,rmihaylov,['arxiv.org/abs/1810.04805']
unicamp-dl/ptt5-base-en-pt-msmarco-10k-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
christofid/pgt,christofid,[{'title': 'PGT: a prompt based generative transformer for the patent domain'}]
julien-c/kan-bayashi-jsut_tts_train_tacotron2_ja,julien-c,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
julien-c/mini_an4_asr_train_raw_bpe_valid,julien-c,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
yhavinga/t5-small-24L-dutch-english,yhavinga,['arxiv.org/abs/2109.10686']
neuralmagic/oBERT-3-downstream-dense-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
obss/mt5-small-3task-highlight-combined3,obss,"['arxiv.org/abs/2111.06476', {'title': 'Automated question generation and question answering from Turkish texts using text-to-text transformers'}]"
rbawden/CCASS-semi-auto-titrages-base,rbawden,['bibtex']
espnet/anogkongda_librimix_enh_train_raw_valid.si_snr.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kamo-naoyuki_mini_an4_asr_train_raw_bpe_valid.acc.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-1000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/distilbert-base-en-fr-uk-el-ro-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-bg-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
ibraheemmoosa/xlmindic-base-multiscript,ibraheemmoosa,[{'title': 'Does Transliteration Help Multilingual Language Modeling?'}]
nateraw/lightweight-gan-test,nateraw,['bibtex']
thu-coai/EVA1.0,thu-coai,"['arxiv.org/abs/2108.01547', {'title': 'EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training'}]"
nvidia/stt_ca_conformer_ctc_large,nvidia,['arxiv.org/abs/2005.08100']
MultiBertGunjanPatrick/multiberts-seed-4-140k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/distilbert-base-en-pt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
fav-kky/FERNET-CC_sk,fav-kky,['arxiv.org/abs/2107.10042']
MultiBertGunjanPatrick/multiberts-seed-2-1400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/distilbert-base-en-el-ru-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian,Edresson,['arxiv.org/abs/2204.00618']
MultiBertGunjanPatrick/multiberts-seed-3-1800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-1400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-1900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-180k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/turkish_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
mideind/IceBERT-mC4-is,mideind,"['arxiv.org/abs/2201.05601', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-1700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Davlan/byt5-base-eng-yor-mt,Davlan,['arxiv.org/abs/2103.08647']
phjhk/hklegal-xlm-r-base-t,phjhk,"['arxiv.org/abs/1911.02116', {'title': 'Unsupervised Cross-lingual Representation Learning at Scale'}]"
MultiBertGunjanPatrick/multiberts-seed-2-300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/distilbert-base-en-zh-hi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-0-120k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
ufal/byt5-small-multilexnorm2021-tr,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-20,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/brianyan918_iwslt22_dialect_transformer_fisherlike,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
halice/token-learner,halice,['arxiv.org/abs/2010.11929']
MultiBertGunjanPatrick/multiberts-seed-1-800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-russian,Edresson,['arxiv.org/abs/2204.00618']
Goud/DarijaBERT-summarization-goud,Goud,[{'title': 'Goud.ma: a News Article Dataset for Summarization in Moroccan Darija'}]
espnet/americasnlp22-asr-tav,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/distilbert-base-en-lt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/bert-base-en-uk-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-3-1100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Intel/bert-large-uncased-sparse-80-1x4-block-pruneofa,Intel,['arxiv.org/abs/2111.05754']
MultiBertGunjanPatrick/multiberts-seed-3-40k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Craig/paraphrase-MiniLM-L6-v2,Craig,"['arxiv.org/abs/1908.10084', 'bibtex']"
flax-community/wav2vec2-dhivehi,flax-community,['arxiv.org/abs/2006.11477']
fgaim/tielectra-geezswitch,fgaim,[{'title': 'GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages'}]
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
Helsinki-NLP/opus-mt-tc-big-zls-zle,Helsinki-NLP,['bibtex']
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-90,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
Addedk/kbbert-distilled-cased,Addedk,['arxiv.org/abs/2103.06418']
AiLab-IMCS-UL/lvbert,AiLab-IMCS-UL,['bibtex']
rmihaylov/bert-base-pos-theseus-bg,rmihaylov,['arxiv.org/abs/1810.04805']
Helsinki-NLP/opus-mt-tc-base-bat-zle,Helsinki-NLP,['bibtex']
Geotrend/bert-base-en-fr-da-ja-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
tau/t5-v1_1-large-sled,tau,"['arxiv.org/abs/2208.00748', {'title': 'Efficient Long-Text Understanding with Short-Text Models'}]"
UWB-AIR/MQDD-duplicates,UWB-AIR,"['arxiv.org/abs/2203.14093', 'doi.org/10.48550/arxiv.2203.14093,']"
Helsinki-NLP/opus-mt-tc-big-en-lv,Helsinki-NLP,['bibtex']
Helsinki-NLP/opus-mt-tc-big-zlw-zle,Helsinki-NLP,['bibtex']
it5/it5-base-ilgiornale-to-repubblica,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
Davlan/m2m100_418M-eng-yor-mt,Davlan,['arxiv.org/abs/2103.08647']
mideind/IceBERT-ic3,mideind,"['arxiv.org/abs/2201.05601', 'bibtex']"
espnet/kan-bayashi_jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-d57a28,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
pyf98/aishell_branchformer_fast_selfattn_e24_amp,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/zh-CN_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian,Edresson,['arxiv.org/abs/2204.00618']
espnet/Hoon_Chung_jsut_asr_train_asr_conformer8_raw_char_sp_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
mideind/IceBERT-xlmr-ic3,mideind,"['arxiv.org/abs/2201.05601', 'bibtex']"
Geotrend/bert-base-uk-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
fusing/ddpm-lsun-bedroom,fusing,['arxiv.org/abs/2006.11239']
MultiBertGunjanPatrick/multiberts-seed-0-600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-12-downstream-pruned-unstructured-97-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-downstream-pruned-unstructured-80-mnli,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
Geotrend/distilbert-base-en-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
neuralmagic/oBERT-12-downstream-pruned-unstructured-97-mnli,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
anlausch/aq_bert_gaq_mt,anlausch,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-0-800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-12-downstream-pruned-unstructured-97-qqp,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
nateraw/lightweight-gan-pokemon,nateraw,['bibtex']
neuralmagic/oBERT-12-downstream-pruned-unstructured-90-qqp,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-teacher-qqp,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-downstream-pruned-block4-90-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-downstream-dense-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-downstream-pruned-block4-90-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-3-downstream-dense-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-6-downstream-pruned-unstructured-80-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-upstream-pruned-unstructured-90-v2,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
MultiBertGunjanPatrick/multiberts-seed-2-1500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Muennighoff/SGPT-125M-mean-nli,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
espnet/Hoon_Chung_zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
jhu-clsp/LegalBert,jhu-clsp,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-2-1200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-18,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-21,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Xuandong/HPD-TinyBERT-F128,Xuandong,"['arxiv.org/abs/2203.07687', {'title': 'Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation'}]"
benjamin/gpt2-wechsel-malagasy,benjamin,['bibtex']
espnet/Yen-Ju_Lu_l3das22_enh_train_dprnntac_fasnet_valid.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
nvidia/stt_en_citrinet_768_ls,nvidia,['arxiv.org/abs/2104.01721']
dbmdz/bert-medium-historic-multilingual-cased,dbmdz,['arxiv.org/abs/1908.08962']
nvidia/stt_en_citrinet_256_ls,nvidia,['arxiv.org/abs/2104.01721']
MultiBertGunjanPatrick/multiberts-seed-3-1000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
rampasek/prot_bert_bfd_rosetta204060aa,rampasek,['doi.org/10.1101/2020.07.12.199554)']
rampasek/prot_bert_bfd_rosetta20aa,rampasek,['doi.org/10.1101/2020.07.12.199554)']
ElMuchoDingDong/AudreyBotBlenderBot,ElMuchoDingDong,['arxiv.org/abs/2004.13637']
MultiBertGunjanPatrick/multiberts-seed-4-300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-1100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/bert-base-en-fr-uk-el-ro-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/kan-bayashi_jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-120k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-40k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/marathi_openslr64,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/distilbert-base-en-ro-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
UWB-AIR/MQDD-pretrained,UWB-AIR,"['arxiv.org/abs/2203.14093', 'doi.org/10.48550/arxiv.2203.14093,']"
MultiBertGunjanPatrick/multiberts-seed-1-600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all,cardiffnlp,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-1-500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
ahnafsamin/Tacotron2-gronings,ahnafsamin,['arxiv.org/abs/1712.05884']
MultiBertGunjanPatrick/multiberts-seed-1-200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020,cardiffnlp,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-2-200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/kan-bayashi_jsut_conformer_fastspeech2_accent,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-80k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
GroNLP/bert-base-dutch-cased-upos-alpino-gronings,GroNLP,['arxiv.org/abs/2105.02855']
Geotrend/distilbert-base-en-da-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-4-1500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
byeongal/gpt2-large,byeongal,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
MultiBertGunjanPatrick/multiberts-seed-0-1600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-11,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-0k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-20k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-12-downstream-pruned-unstructured-90-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1,madlag,['arxiv.org/abs/2005.07683']
Geotrend/distilbert-base-en-th-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-en-fr-nl-ru-ar-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-en-fr-zh-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
nielsr/tapex-large-finetuned-wtq,nielsr,['arxiv.org/abs/2107.07653']
MultiBertGunjanPatrick/multiberts-seed-1-80k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-180k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-14,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/kamo-naoyuki_timit_asr_train_asr_raw_word_valid.acc.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
sw005320/Shinji_Watanabe_laborotv_asr_train_blstm,sw005320,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-20k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
RUCAIBox/mtl-story,RUCAIBox,"['arxiv.org/abs/2206.12131', {'title': 'MVP: Multi-task Supervised Pre-training for Natural Language Generation'}]"
MultiBertGunjanPatrick/multiberts-seed-17,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-teacher-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
MultiBertGunjanPatrick/multiberts-seed-3-400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
ufal/byt5-small-multilexnorm2021-de,ufal,"['arxiv.org/abs/2105.13626', 'bibtex']"
yhavinga/t5-v1_1-base-dutch-english-cased-1024,yhavinga,['arxiv.org/abs/2109.10686']
espnet/tamil_slu,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
asapp/sew-d-mid-400k-ft-ls100h,asapp,['arxiv.org/abs/2109.06870']
yhavinga/t5-v1_1-base-dutch-english-cased,yhavinga,['arxiv.org/abs/2109.10686']
espnet/thai_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
qarib/bert-base-qarib_far_8280k,qarib,[{'title': 'Pre-Training BERT on Arabic Tweets: Practical Considerations'}]
facebook/wav2vec2-base-el-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
DrishtiSharma/lwg_chebakia,DrishtiSharma,['bibtex']
facebook/wav2vec2-base-hr-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
facebook/spar-paq-bm25-lexmodel-context-encoder,facebook,['arxiv.org/abs/2110.06918']
espnet/ftshijt_espnet2_asr_dsing_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/spar-marco-bm25-lexmodel-context-encoder,facebook,['arxiv.org/abs/2110.06918']
facebook/wav2vec2-large-el-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
espnet/slurp_slu_2pass_gt,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/wav2vec2-base-lv-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
facebook/wav2vec2-large-west_germanic-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
fgaim/tielectra-small-pos,fgaim,['bibtex']
Geotrend/distilbert-base-en-fr-da-ja-vi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
MultiBertGunjanPatrick/multiberts-seed-0-1400k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Geotrend/distilbert-base-lt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_conformer_fastspeech2_tacotron2_prosody,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
tner/bert-large-tweetner7-2021,tner,['bibtex']
pyf98/librispeech_conformer_layerdrop0.1_last6,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
it5/mt5-small-repubblica-to-ilgiornale,it5,"['arxiv.org/abs/2203.03759', {'title': '{IT5'}]"
l3cube-pune/hindi-bert-v1,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
Helsinki-NLP/opus-mt-tc-big-zle-fi,Helsinki-NLP,['bibtex']
Geotrend/bert-base-en-nl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/Karthik_DSTC2_asr_train_asr_Hubert_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Helsinki-NLP/opus-mt-tc-base-uk-tr,Helsinki-NLP,['bibtex']
darragh/swinunetr-btcv-base,darragh,[{'title': 'Self-supervised pre-training of swin transformers for 3d medical image analysis'}]
fgaim/tielectra-small-sentiment,fgaim,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-3-800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
facebook/wav2vec2-base-et-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc_inference_only,deepdoctection,['arxiv.org/abs/1911.10683']
frollo/word2vec-for-crime-categorization,frollo,"['doi.org/10.1007/978-981-15-5093-5_13', 'bibtex']"
matjesg/deepflash2_demo,matjesg,"['doi.org/10.7554/eLife.59780),']"
flax-community/gpt-2-german,flax-community,"['arxiv.org/abs/1904.09751', 'doi.org/10.1145/3442188.3445922},', 'bibtex']"
matrix/test,matrix,['arxiv.org/abs/1810.04805']
mechanicalsea/speecht5-tts,mechanicalsea,[{'title': '{S'}]
mechanicalsea/lighthubert,mechanicalsea,"['arxiv.org/abs/2203.15610', {'title': '{LightHuBERT'}]"
mechanicalsea/efficient-tdnn,mechanicalsea,"['arxiv.org/abs/2103.13581', {'title': '{EfficientTDNN'}]"
kunheekim/style-aware-discriminator,kunheekim,['arxiv.org/abs/2203.15375']
lgris/distilxlsr_bp_16-24,lgris,['arxiv.org/abs/2110.01900']
mechanicalsea/speecht5-vc,mechanicalsea,[{'title': '{S'}]
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc,deepdoctection,['arxiv.org/abs/1911.10683']
Skoltech/russian-sensitive-topics,Skoltech,"['arxiv.org/abs/2103.05345', 'bibtex']"
matjesg/cFOS_in_HC,matjesg,"['doi.org/10.7554/eLife.59780),']"
lgris/distilxlsr_bp_4-12,lgris,['arxiv.org/abs/2110.01900']
lgris/distilxlsr_bp_8-12-24,lgris,['arxiv.org/abs/2110.01900']
cisco-ai/tts-maui-obama,cisco-ai,"[{'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
facebook/wav2vec2-large-mt-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
dslack/all-finetuned-ttm-models,dslack,['arxiv.org/abs/2207.04154']
RVN/XLMR-MaCoCu-tr,RVN,['bibtex']
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet_inference_only,deepdoctection,['arxiv.org/abs/1908.07836']
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet,deepdoctection,['arxiv.org/abs/1908.07836']
kingabzpro/CELEB-GANs,kingabzpro,['bibtex']
mhyatt000/YOLOv5,mhyatt000,['doi.org/10.5281/zenodo.6222936}']
hitachinsk/FGT,hitachinsk,['arxiv.org/abs/2208.06768']
pyronear/mobilenet_v3_large,pyronear,"['arxiv.org/abs/1905.02244', 'bibtex']"
l3cube-pune/hindi-albert,l3cube-pune,"[""doi.org/10.13140/RG.2.2.14606.84809'>"", 'bibtex']"
canIjoin/datafun,canIjoin,[{'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}]
nghuyong/ernie-1.0,nghuyong,"['arxiv.org/abs/1904.09223', {'title': 'Ernie: Enhanced representation through knowledge integration'}]"
dathudeptrai/tts-tacotron2-synpaflex-fr,dathudeptrai,"['arxiv.org/abs/1712.05884', 'bibtex']"
fusing/ddim-lsun-church,fusing,['arxiv.org/abs/2010.02502']
cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020,cardiffnlp,['bibtex']
fusing/ddim-celeba-hq_copy,fusing,['arxiv.org/abs/2010.02502']
nghuyong/ernie-2.0-en,nghuyong,"['arxiv.org/abs/1907.12412', {'title': 'ERNIE 2.0: A Continual Pre-training Framework for Language Understanding'}]"
facebook/wav2vec2-base-10k-voxpopuli-ft-et,facebook,['arxiv.org/abs/2101.00390']
espnet/belarusian_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
desh2608/icefall-asr-spgispeech-pruned-transducer-stateless2,desh2608,['arxiv.org/abs/2104.02014']
fusing/glide-base,fusing,['arxiv.org/abs/2112.10741']
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c,deepdoctection,['arxiv.org/abs/1911.10683']
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c_inference_only,deepdoctection,['arxiv.org/abs/1911.10683']
deepparag/gpt-j-6B-longer-generation,deepparag,['arxiv.org/abs/2104.09864']
maze/FastStyleTransfer,maze,['arxiv.org/abs/1603.08155']
deepdoctection/d2_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc_inference_only,deepdoctection,['arxiv.org/abs/1911.10683']
deepdoctection/d2_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c_inference_only,deepdoctection,['arxiv.org/abs/1911.10683']
deepdoctection/d2_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet_inference_only,deepdoctection,['arxiv.org/abs/1908.07836']
nghuyong/ernie-tiny,nghuyong,[{'title': 'ERNIE 2.0: A Continual Pre-training Framework for Language Understanding'}]
malteos/specter-wol,malteos,['arxiv.org/abs/2202.06671']
mishig/test_regex_searchreplace,mishig,"['arxiv.org/abs/2105.01051', {'title': 'SUPERB: Speech processing Universal PERformance Benchmark'}]"
huggan/distill-ccld-wa,huggan,['arxiv.org/abs/2112.10752']
huggan/fastgan-few-shot-grumpy-cat,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
huggan/fastgan-few-shot-universe,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
huggan/NeonGAN,huggan,['arxiv.org/abs/1703.10593']
jj-co/gtr-t5-base,jj-co,['arxiv.org/abs/2112.07899']
julien-c/voice-activity-detection,julien-c,"['arxiv.org/abs/1910.10655', 'bibtex']"
google/t5-11b-ssm-wqo,google,['arxiv.org/abs/1910.10683']
julien-c/fasttext-language-id,julien-c,[{'title': 'Bag of Tricks for Efficient Text Classification'}]
keras-io/image-captioning,keras-io,['arxiv.org/abs/1502.03044']
katielink/brats_mri_segmentation_v0.1.0,katielink,['arxiv.org/abs/1810.11654']
katielink/spleen_ct_segmentation_v0.1.0,katielink,"['arxiv.org/abs/1811.12506', 'doi.org/10.1007/978-3-030-12029-0_40']"
fusing/ddpm-lsun-bedroom-ema,fusing,['arxiv.org/abs/2006.11239']
dnouri/ventricular_short_axis_3label,dnouri,['doi.org/10.1007/978-3-030-12029-0_40`']
dnouri/spleen_ct_segmentation,dnouri,"['arxiv.org/abs/1811.12506', 'doi.org/10.1007/978-3-030-12029-0_40']"
keras-io/swin-transformers,keras-io,['arxiv.org/abs/2103.14030']
dnouri/brats_mri_segmentation,dnouri,['arxiv.org/abs/1810.11654']
facebook/ic_gan,facebook,"['arxiv.org/abs/2109.05070', {'title': 'Instance-Conditioned GAN'}]"
huggan/fastgan-few-shot-aurora,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
huggan/pix2pix-maps,huggan,"['arxiv.org/abs/1611.07004', 'bibtex']"
hackathon-pln-es/unam_tesis_ROBERTA_GOB_finnetuning,hackathon-pln-es,"[{'title': ""Unam's thesis with PlanTL-GOB-ES/roberta-large-bne classify ""}]"
facebook/wav2vec2-base-10k-voxpopuli-ft-lt,facebook,['arxiv.org/abs/2101.00390']
huggan/TediGAN_sketch,huggan,"['arxiv.org/abs/2012.03308', {'title': 'TediGAN: Text-Guided Diverse Face Image Generation and Manipulation'}]"
huggan/pix2pix-night2day,huggan,"['arxiv.org/abs/1611.07004', {'title': 'Image-to-Image Translation with Conditional Adversarial Networks'}]"
huggan/fastgan-few-shot-fauvism-still-life,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
huggan/pix2pix-maps-test,huggan,['bibtex']
facebook/wav2vec2-base-sl-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
huggan/pix2pix-uavid-15,huggan,"['arxiv.org/abs/1611.07004', {'title': 'Image-to-Image Translation with Conditional Adversarial Networks'}]"
huggan/sim2real_cyclegan,huggan,"['arxiv.org/abs/2104.13395', 'doi.org/10.48550/arxiv.1703.10593,']"
espnet/americasnlp22-asr-gvc,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
drcod/DagaareBERTa,drcod,['arxiv.org/abs/1907.11692']
huggan/fastgan-few-shot-shells,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
hadidev/gpt2-urdu-tokenizer-withgpt2,hadidev,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
kjackson/distilbert-base-uncased-finetuned-emotion,kjackson,['arxiv.org/abs/1907.11692']
huggan/fastgan-few-shot-painting,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
huggan/pix2pix-edge2shoes,huggan,"['arxiv.org/abs/1611.07004', {'title': 'Image-to-Image Translation with Conditional Adversarial Networks'}]"
hugginglearners/ml-news-classify-fastai,hugginglearners,[{'title': 'AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages'}]
clarin-pl/fastText-kgr10,clarin-pl,['bibtex']
facebook/wav2vec2-base-mt-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
hasibzunair/melanet,hasibzunair,"['arxiv.org/abs/2004.06824', {'title': 'Melanoma detection using adversarial training and deep transfer learning'}]"
facebook/wav2vec2-large-baltic-voxpopuli-v2,facebook,['arxiv.org/abs/2101.00390']
gwang-kim/DiffusionCLIP-CelebA_HQ,gwang-kim,"['arxiv.org/abs/2110.02711', {'title': 'Diffusionclip: Text-guided image manipulation using diffusion models'}]"
gwang-kim/DiffusionCLIP-LSUN_Bedroom,gwang-kim,"['arxiv.org/abs/2110.02711', {'title': 'Diffusionclip: Text-guided image manipulation using diffusion models'}]"
huggan/fastgan-few-shot-moongate,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
huggan/fastgan-few-shot-anime-face,huggan,"['arxiv.org/abs/2101.04775', {'title': 'Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis'}]"
nateraw/yolov6t,nateraw,['arxiv.org/abs/1910.09700']
npc-engine/exported-flowtron-waveglow-librispeech-tts,npc-engine,['arxiv.org/abs/2005.05957']
npc-engine/exported-bart-light-gail-chatbot,npc-engine,['arxiv.org/abs/2004.13796']
nousr/conditioned-prior,nousr,['arxiv.org/abs/2204.06125']
TinFernandez/dummy,TinFernandez,"['arxiv.org/abs/1810.04805', 'bibtex']"
nateraw/yolov6s,nateraw,['arxiv.org/abs/1910.09700']
nvidia/stt_en_citrinet_1024_ls,nvidia,['arxiv.org/abs/2104.01721']
aharley/pips,aharley,"['arxiv.org/abs/2204.04153', {'title': 'Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories'}]"
anhdungitvn/vi-xlm-roberta-large,anhdungitvn,[{'title': 'x'}]
nateraw/yolov6n,nateraw,['arxiv.org/abs/1910.09700']
YushiUeda/callhome_adapt_real,YushiUeda,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
bigscience/bloom-optimizer-states,bigscience,['arxiv.org/abs/1909.08053']
akhaliq/YOLOP,akhaliq,['arxiv.org/abs/2108.11250']
Visual-Attention-Network/VAN-Tiny-original,Visual-Attention-Network,"['arxiv.org/abs/2202.09741', {'title': 'Visual Attention Network'}]"
nateraw/ventricular_short_axis_3label,nateraw,['doi.org/10.1007/978-3-030-12029-0_40`']
jpwahle/xlnet-base-plagiarism-detection,jpwahle,['doi.org/10.5281/zenodo.3608000)']
Visual-Attention-Network/VAN-Small-original,Visual-Attention-Network,"['arxiv.org/abs/2202.09741', {'title': 'Visual Attention Network'}]"
Visual-Attention-Network/VAN-Base-original,Visual-Attention-Network,"['arxiv.org/abs/2202.09741', {'title': 'Visual Attention Network'}]"
nateraw/my-cool-model-with-card-2,nateraw,['bibtex']
nateraw/new-modelcard-template-test,nateraw,['arxiv.org/abs/1910.09700']
nateraw/test-hf-hub-modelcards-compatibility,nateraw,['bibtex']
nateraw/modelcard-creator-test,nateraw,['arxiv.org/abs/1910.09700']
nateraw/modelcard-creator-demo,nateraw,"['arxiv.org/abs/1910.09700', 'doi.org/10.1145%2F3287560.3287596},', {'doi': '10.1145/3287560.3287596'}]"
nateraw/hf-hub-modelcards-pr-test,nateraw,['arxiv.org/abs/1910.09700']
nateraw/nu-wave-x2,nateraw,['bibtex']
nateraw/my-cool-model-with-eval-results,nateraw,['bibtex']
nateraw/my-cool-model-with-card,nateraw,['bibtex']
Visual-Attention-Network/VAN-Large-original,Visual-Attention-Network,"['arxiv.org/abs/2202.09741', {'title': 'Visual Attention Network'}]"
nielsr/pix2pix-cityscapes,nielsr,['bibtex']
Projeto/LegalNLP,Projeto,"['arxiv.org/abs/2110.15709', {'title': 'LegalNLP--Natural Language Processing methods for the Brazilian Legal Language'}]"
Sense-X/uniformer_image,Sense-X,['arxiv.org/abs/2201.09450']
SaulLu/albert-bn-dev,SaulLu,['bibtex']
camusean/grasp_diffusion,camusean,['arxiv.org/abs/2209.03855']
rajeshradhakrishnan/ml-news-classify-fastai,rajeshradhakrishnan,[{'title': 'AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages'}]
sagar122/xperimentalilst_hackathon_2022,sagar122,"['arxiv.org/abs/2205.02455', 'bibtex']"
arjundd/vortex-release,arjundd,['arxiv.org/abs/2111.02549']
arjundd/noise2recon-release,arjundd,['arxiv.org/abs/2110.00075']
Sense-X/uniformer_video,Sense-X,['arxiv.org/abs/2201.04676']
chaitu619/chai_librispeech_asr_train_transducer_v2_raw_en_bpe5000_sp,chaitu619,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
bigscience-catalogue-lm-data/sgpt-nli-bloom-1b3,bigscience-catalogue-lm-data,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
SamMorgan/yolo_v4_tflite,SamMorgan,['arxiv.org/abs/2004.10934']
rsuwaileh/IDRISI-LMR-AR-timebased-typeless,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
Riser/YOLOP,Riser,['arxiv.org/abs/2108.11250']
rsuwaileh/IDRISI-LMR-AR-random-typeless,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
arjundd/dosma-models,arjundd,"[{'title': 'DOSMA: A deep-learning, open-source framework for musculoskeletal MRI analysis'}]"
rsuwaileh/IDRISI-LMR-AR-timebased-typebased,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
Rongjiehuang/ProDiff,Rongjiehuang,"['arxiv.org/abs/2204.09934', {'title': 'ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech'}]"
rsuwaileh/IDRISI-LMR-AR-random-typebased,rsuwaileh,"[{'title': 'When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets'}]"
rufimelo/Legal-SBERTimbau-nli-base-MA,rufimelo,['bibtex']
fav-kky/FERNET-News_sk,fav-kky,['arxiv.org/abs/2107.10042']
fcakyon/timesformer,fcakyon,['arxiv.org/abs/2102.05095']
espnet/simpleoier_chime4_enh_asr_convtasnet_init_noenhloss_wavlm_transformer_init_raw_en_char,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
asapp/sew-d-base-plus-400k,asapp,['arxiv.org/abs/2109.06870']
merve/20newsgroups,merve,['bibtex']
espnet/byan_librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_ac-truncated-68a97b,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/chai_microsoft_indian_langs_te,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
cwkeam/m-ctc-t-large-frame-lid,cwkeam,"['arxiv.org/abs/2111.00161', {'title': 'Pseudo-Labeling for Massively Multilingual Speech Recognition'}]"
aliosm/ai-soco-cpp-roberta-tiny,aliosm,['bibtex']
osanseviero/swin_unetr_btcv_segmentation,osanseviero,['arxiv.org/abs/2201.01266']
bluebalam/paper-rec,bluebalam,['arxiv.org/abs/2109.03955']
aliprf/ACR-Loss,aliprf,[{'title': 'ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment'}]
aliosm/ai-soco-cpp-roberta-tiny-96-clas,aliosm,['bibtex']
aliprf/ASMNet,aliprf,[{'title': 'ASMNet: A Lightweight Deep Neural Network for Face Alignment and Pose Estimation'}]
aliosm/ai-soco-cpp-roberta-tiny-clas,aliosm,['bibtex']
olympictafira/cAT,olympictafira,['arxiv.org/abs/2207.12598']
aliprf/KD-Loss,aliprf,"['arxiv.org/abs/2111.07047', {'title': 'Facial landmark points detection using knowledge distillation-based neural networks'}]"
aliosm/ai-soco-cpp-roberta-small,aliosm,['bibtex']
aliosm/ai-soco-cpp-roberta-tiny-96,aliosm,['bibtex']
aliosm/ai-soco-cpp-roberta-small-clas,aliosm,['bibtex']
osanseviero/magma,osanseviero,['arxiv.org/abs/2112.05253']
Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave,Yulinfeng,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
bhadi26/hadi-rebecca-test-model-public,bhadi26,"['arxiv.org/abs/1907.11692', 'bibtex']"
Yukang/FocalsConv,Yukang,"['arxiv.org/abs/2204.12463', {'title': 'Focal Sparse Convolutional Networks for 3D Object Detection'}]"
Yulinfeng/wsj0_2mix_enh_train_enh_dpcl_raw_valid.si_snr.ave,Yulinfeng,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
aajrami/bert-sr-base,aajrami,"['arxiv.org/abs/2203.10415', {'title': 'How does the pre-training objective affect what large language models learn about linguistic properties?'}]"
opetrova/face-frontalization,opetrova,['arxiv.org/abs/1704.04086']
TencentMedicalNet/MedicalNet-Resnet34,TencentMedicalNet,"['arxiv.org/abs/1904.00625', {'title': 'Med3D: Transfer Learning for 3D Medical Image Analysis'}]"
TencentMedicalNet/MedicalNet-Resnet18,TencentMedicalNet,"['arxiv.org/abs/1904.00625', {'title': 'Med3D: Transfer Learning for 3D Medical Image Analysis'}]"
TencentMedicalNet/MedicalNet-Resnet10,TencentMedicalNet,"['arxiv.org/abs/1904.00625', {'title': 'Med3D: Transfer Learning for 3D Medical Image Analysis'}]"
TencentMedicalNet/MedicalNet-Resnet50,TencentMedicalNet,"['arxiv.org/abs/1904.00625', {'title': 'Med3D: Transfer Learning for 3D Medical Image Analysis'}]"
TencentMedicalNet/MedicalNet-Resnet200,TencentMedicalNet,"['arxiv.org/abs/1904.00625', {'title': 'Med3D: Transfer Learning for 3D Medical Image Analysis'}]"
TencentMedicalNet/MedicalNet-Resnet152,TencentMedicalNet,"['arxiv.org/abs/1904.00625', {'title': 'Med3D: Transfer Learning for 3D Medical Image Analysis'}]"
TencentMedicalNet/MedicalNet-Resnet101,TencentMedicalNet,"['arxiv.org/abs/1904.00625', {'title': 'Med3D: Transfer Learning for 3D Medical Image Analysis'}]"
mudes/multilingual-base,mudes,"['arxiv.org/abs/2102.09665', {'title': '{MUDES: Multilingual Detection of Offensive Spans'}]"
bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m,bookbot,['arxiv.org/abs/2111.09296']
vasugoel/K-12BERT,vasugoel,"['arxiv.org/abs/2205.12335', 'doi.org/10.48550/arxiv.2205.12335,']"
rufimelo/Legal-SBERTimbau-nli-base,rufimelo,['bibtex']
AbhirupGhosh/opus-mt-finetuned-hi-en,AbhirupGhosh,['arxiv.org/abs/1706.03762']
Skoltech/russian-inappropriate-messages,Skoltech,['bibtex']
TalTechNLP/voxlingua107-xls-r-300m-wav2vec,TalTechNLP,[{'title': '{VoxLingua107'}]
THUDM/CogView2,THUDM,"['arxiv.org/abs/2204.14217', {'title': 'CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers'}]"
Sussybaka/gpt2wilkinscoffee,Sussybaka,"['arxiv.org/abs/1910.01108', {'title': 'DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter'}]"
xyma/PROP-marco,xyma,"['doi.org/10.1145/3437963.3441777},', 'bibtex']"
rufimelo/Legal-SBERTimbau-nli-large,rufimelo,['bibtex']
baudm/crnn,baudm,[{'title': 'An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition'}]
darragh/swinunetr-btcv-small,darragh,[{'title': 'Self-supervised pre-training of swin transformers for 3d medical image analysis'}]
espnet/id_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/bert-base-en-it-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
RVN/MaltBERTa,RVN,['bibtex']
fusing/ddpm-lsun-cat-ema,fusing,['arxiv.org/abs/2006.11239']
espnet/kyrgyz_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
DCU-NLP/electra-base-irish-cased-discriminator-v1,DCU-NLP,"['arxiv.org/abs/2107.12930', 'bibtex']"
xyma/PROP-marco-step400k,xyma,"['doi.org/10.1145/3437963.3441777},', 'bibtex']"
cwkeam/m-ctc-t-large-lid,cwkeam,"['arxiv.org/abs/2111.00161', {'title': 'Pseudo-Labeling for Massively Multilingual Speech Recognition'}]"
OWG/convbert-base-spanish,OWG,['arxiv.org/abs/2008.02496']
unicamp-dl/ptt5-base-pt-msmarco-10k-v1,unicamp-dl,['arxiv.org/abs/2108.13897']
OWG/DeiT,OWG,['arxiv.org/abs/2012.12877']
OWG/bigbird-roberta-base,OWG,['arxiv.org/abs/2007.14062']
zaccharieramzi/XPDNet-brain-af4,zaccharieramzi,"['arxiv.org/abs/2010.07290', 'bibtex']"
OWG/resnet-50,OWG,['arxiv.org/abs/1512.03385']
zaccharieramzi/PDNet-OASIS,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zaccharieramzi/CascadeNet-OASIS,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zaccharieramzi/UNet-fastmri,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zaccharieramzi/UNet-OASIS,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zaccharieramzi/CascadeNet-fastmri,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zaccharieramzi/KIKI-net-fastmri,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zaccharieramzi/KIKI-net-OASIS,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zuppif/dummy,zuppif,['arxiv.org/abs/2003.13678']
bookbot/wav2vec2-adult-child-id-cls,bookbot,['arxiv.org/abs/2006.11477']
schhwmn/mt5-base-finetuned-ukr-gec,schhwmn,['arxiv.org/abs/2103.16997']
yellowjs0304/lmv2large,yellowjs0304,['arxiv.org/abs/2012.14740']
niklaspm/linkbert-base-finetuned-squad,niklaspm,['arxiv.org/abs/2203.15827']
MultiBertGunjanPatrick/multiberts-seed-1-140k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
nielsr/coref-bert-large,nielsr,['arxiv.org/abs/2004.06870']
pyronear/resnet34,pyronear,"['arxiv.org/abs/1512.03385', 'bibtex']"
pyf98/speechcommands_12commands_conformer,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
ahnafsamin/FastSpeech2-gronings,ahnafsamin,['arxiv.org/abs/2006.04558']
Geotrend/bert-base-en-pt-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
espnet/kan-bayashi_jsut_transformer_accent,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/distilbert-base-uk-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-ro-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
asapp/sew-d-mid-400k,asapp,['arxiv.org/abs/2109.06870']
Davlan/m2m100_418M-yor-eng-mt,Davlan,['arxiv.org/abs/2103.08647']
Geotrend/bert-base-en-fr-es-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Yaxin/ernie_2.0_skep_large_en,Yaxin,[{'title': 'SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis'}]
Geotrend/distilbert-base-en-hi-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Geotrend/distilbert-base-en-de-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
NimaBoscarino/albert-nima,NimaBoscarino,"['arxiv.org/abs/1908.10084', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-1000k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/kan-bayashi_jsut_full_band_vits_accent_with_pause,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
lgris/distilxlsr_bp_12-16,lgris,['arxiv.org/abs/2110.01900']
globuslabs/ScholarBERT-XL_1,globuslabs,['arxiv.org/abs/2205.11342']
asapp/sew-d-base-plus-100k,asapp,['arxiv.org/abs/2109.06870']
juletxara/vilt-vsr-random,juletxara,"['arxiv.org/abs/2205.00363', {'title': 'Visual Spatial Reasoning'}]"
juletxara/vilt-vsr-zeroshot,juletxara,"['arxiv.org/abs/2205.00363', {'title': 'Visual Spatial Reasoning'}]"
espnet/roshansh_asr_base_sp_conformer_swbd,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/dns_icassp21_enh_train_enh_tcn_tf_raw,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Geotrend/bert-base-en-fr-lt-no-pl-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
tftransformers/gpt2-medium,tftransformers,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
benjamin/gpt2-wechsel-scottish-gaelic,benjamin,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-2-900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-1700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-24,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-12-downstream-pruned-block4-80-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-6-downstream-pruned-unstructured-90-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-3-downstream-pruned-block4-80-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-6-downstream-pruned-block4-90-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-6-downstream-pruned-block4-80-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-6-downstream-dense-QAT-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
neuralmagic/oBERT-12-downstream-pruned-unstructured-80-qqp,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
MultiBertGunjanPatrick/multiberts-seed-0-160k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-1300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
fusing/ddpm-lsun-church-ema,fusing,['arxiv.org/abs/2006.11239']
MultiBertGunjanPatrick/multiberts-seed-2-120k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-1-1500k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-1800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-1300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-180k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-1700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-1900k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-200k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-160k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
moha/arabert_arabic_covid19,moha,['arxiv.org/abs/2004.04315']
byeongal/gpt2,byeongal,[{'title': 'Language Models are Unsupervised Multitask Learners'}]
espnet/kamo-naoyuki_wsj_transformer2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
pyf98/speechcommands_35commands_conformer,pyf98,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Zhaoheng/svoice_wsj0_2mix,Zhaoheng,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/kan-bayashi_jsut_fastspeech,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-1800k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-12,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-23,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-160k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1,neuralmagic,"['arxiv.org/abs/2203.07259', {'title': 'The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models'}]"
Muennighoff/SGPT-125M-weightedmean-msmarco,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
MultiBertGunjanPatrick/multiberts-seed-0-1100k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
yhavinga/t5-eff-xl-8l-dutch-english-cased,yhavinga,['arxiv.org/abs/2109.10686']
MultiBertGunjanPatrick/multiberts-seed-2-20k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
asapp/sew-d-mid-k127-400k-ft-ls100h,asapp,['arxiv.org/abs/2109.06870']
MultiBertGunjanPatrick/multiberts-seed-2-700k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-2-600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-60k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
espnet/Yushi_Ueda_mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
facebook/data2vec-audio-large-10m,facebook,['arxiv.org/abs/2202.03555']
espnet/YushiUeda_librimix_diar_enh_2_3_spk_lmf,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/jv_openslr35,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/ftshijt_espnet2_asr_totonac_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/YushiUeda_swbd_sentiment_asr_train_asr_conformer_wav2vec2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/simpleoier_chime4_enh_asr_train_enh_asr_convtasnet_fbank_transformer_raw_en_char,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/YushiUeda_harpervalley_train_asr_hubert_raw_en_word,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/Karthik_DSTC2_asr_train_asr_wav2vec_conformer_2,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
espnet/aaf_openslr57,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
lgris/sew-tiny-pt,lgris,['arxiv.org/abs/2109.06870']
NimaBoscarino/aot-gan-places2,NimaBoscarino,['bibtex']
espnet/kan-bayashi_jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
Saisam/Inquirer_ner_loc,Saisam,[{'title': 'Contextual String Embeddings for Sequence Labeling'}]
espnet/Karthik_DSTC2_asr_train_asr_wav2vec_transformer,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
RVN/XLMR-MaltBERTa,RVN,['bibtex']
espnet/greek_commonvoice_blstm,espnet,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-4-180k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Dimitre/bert_en_cased_preprocess,Dimitre,['arxiv.org/abs/1810.04805']
Dimitre/bert_en_cased_L-12_H-768_A-12,Dimitre,['arxiv.org/abs/1810.04805']
Dimitre/mobilenet_v3_small,Dimitre,['arxiv.org/abs/1905.02244']
torchxrayvision/densenet121-res224-mimic_nb,torchxrayvision,"['arxiv.org/abs/2111.00595', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-3-600k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-0-1300k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
Johnson-Lsx/Shaoxiong_Lin_dns_ins20_enh_enh_train_enh_dccrn_raw,Johnson-Lsx,"['doi.org/10.21437/Interspeech.2018-1456}', 'bibtex']"
sohomghosh/LIPI_FinSim4_ESG_task2,sohomghosh,['bibtex']
slone/fastText-LID-323,slone,['arxiv.org/abs/2209.09368']
skops-ci/hf_hub_example-8de94048-9cfb-4656-8984-4e6fa385feeb,skops-ci,['bibtex']
skops-ci/hf_hub_example-a4b4abaf-d1c3-4300-a5ac-cc9e3c65bd39,skops-ci,['bibtex']
sentence-transformers/average_word_embeddings_levy_dependency,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/clip-ViT-B-16,sentence-transformers,['arxiv.org/abs/2103.00020']
sentence-transformers/clip-ViT-L-14,sentence-transformers,['arxiv.org/abs/2103.00020']
Matthijs/vit-base-patch16-224,Matthijs,['arxiv.org/abs/2010.11929']
Muennighoff/SBERT-base-msmarco-asym,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
Muennighoff/SGPT-125M-weightedmean-msmarco-asym,Muennighoff,"['arxiv.org/abs/2202.08904', {'title': 'SGPT: GPT Sentence Embeddings for Semantic Search'}]"
tensorspeech/tts-fastspeech-ljspeech-en,tensorspeech,"['arxiv.org/abs/1905.09263', 'bibtex']"
tensorspeech/tts-tacotron2-ljspeech-en,tensorspeech,"['arxiv.org/abs/1712.05884', 'bibtex']"
MultiBertGunjanPatrick/multiberts-seed-13,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
sentence-transformers/average_word_embeddings_glove.6B.300d,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/average_word_embeddings_glove.840B.300d,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/average_word_embeddings_komninos,sentence-transformers,"['arxiv.org/abs/1908.10084', 'bibtex']"
sentence-transformers/clip-ViT-B-32,sentence-transformers,['arxiv.org/abs/2103.00020']
Marc/pegasus_xsum_gigaword,Marc,['bibtex']
huggingnft/mini-mutants__2__boredapeyachtclub,huggingnft,"['arxiv.org/abs/1703.10593', 'doi.org/10.48550/arxiv.1703.10593,']"
huggingnft/boredapeyachtclub__2__mutant-ape-yacht-club,huggingnft,"['arxiv.org/abs/1703.10593', 'doi.org/10.48550/arxiv.1703.10593,']"
huggingnft/cryptopunks__2__bored-apes-yacht-club,huggingnft,"['arxiv.org/abs/1703.10593', 'doi.org/10.48550/arxiv.1703.10593,']"
Gxl/234,Gxl,['arxiv.org/abs/1907.11692']
Guldeniz/pix2pix_maps,Guldeniz,['bibtex']
tensorspeech/tts-mb_melgan-thorsten-ger,tensorspeech,['arxiv.org/abs/2005.05106']
tensorspeech/tts-mb_melgan-synpaflex-fr,tensorspeech,['arxiv.org/abs/2005.05106']
tensorspeech/tts-tacotron2-synpaflex-fr,tensorspeech,"['arxiv.org/abs/1712.05884', 'bibtex']"
Graphcore/distilbert-base-ipu,Graphcore,['arxiv.org/abs/1910.01108']
tensorspeech/tts-fastspeech2-ljspeech-en,tensorspeech,['arxiv.org/abs/2006.04558']
tensorspeech/tts-melgan-ljspeech-en,tensorspeech,['arxiv.org/abs/1910.06711']
tensorspeech/tts-tacotron2-thorsten-ger,tensorspeech,"['arxiv.org/abs/1712.05884', 'bibtex']"
tensorspeech/tts-tacotron2-kss-ko,tensorspeech,"['arxiv.org/abs/1712.05884', 'bibtex']"
tensorspeech/tts-fastspeech2-baker-ch,tensorspeech,['arxiv.org/abs/2006.04558']
tensorspeech/tts-tacotron2-baker-ch,tensorspeech,"['arxiv.org/abs/1712.05884', 'bibtex']"
tensorspeech/tts-fastspeech2-kss-ko,tensorspeech,['arxiv.org/abs/2006.04558']
tensorspeech/tts-mb_melgan-ljspeech-en,tensorspeech,['arxiv.org/abs/2005.05106']
tensorspeech/tts-mb_melgan-kss-ko,tensorspeech,['arxiv.org/abs/2005.05106']
tensorspeech/tts-mb_melgan-baker-ch,tensorspeech,['arxiv.org/abs/2005.05106']
Geotrend/distilbert-base-en-sw-cased,Geotrend,[{'title': 'Load What You Need: Smaller Versions of Mutlilingual BERT'}]
Marissa/model-card-testing,Marissa,['arxiv.org/abs/1910.09700']
MONAI/example_spleen_segmentation,MONAI,"['arxiv.org/abs/1811.12506', 'doi.org/10.1007/978-3-030-12029-0_40']"
CVPR/DualStyleGAN,CVPR,"['arxiv.org/abs/2203.13248', 'bibtex']"
NovelAI/genji-python-6B-split,NovelAI,['arxiv.org/abs/2104.09864']
DataikuNLP/average_word_embeddings_glove.6B.300d,DataikuNLP,"['arxiv.org/abs/1908.10084', 'bibtex']"
NimaBoscarino/unicorn_track_tiny_rt_mask,NimaBoscarino,"['arxiv.org/abs/2111.12085', {'title': 'Towards Grand Unification of Object Tracking'}]"
NimaBoscarino/unicorn_track_large_mask,NimaBoscarino,"['arxiv.org/abs/2111.12085', {'title': 'Towards Grand Unification of Object Tracking'}]"
NimaBoscarino/unicorn_track_tiny_mask,NimaBoscarino,"['arxiv.org/abs/2111.12085', {'title': 'Towards Grand Unification of Object Tracking'}]"
NimaBoscarino/unicorn_track_r50_mask,NimaBoscarino,"['arxiv.org/abs/2111.12085', {'title': 'Towards Grand Unification of Object Tracking'}]"
zaccharieramzi/PDNet-fastmri,zaccharieramzi,[{'title': 'Benchmarking MRI reconstruction neural networks on large public datasets'}]
zaccharieramzi/UPDNet-knee-singlecoil-af4,zaccharieramzi,"['arxiv.org/abs/2010.07290', 'bibtex']"
vendorabc/modeltest,vendorabc,['bibtex']
zaccharieramzi/UPDNet-knee-af8,zaccharieramzi,"['arxiv.org/abs/2010.07290', 'bibtex']"
zaccharieramzi/UPDNet-knee-af4,zaccharieramzi,"['arxiv.org/abs/2010.07290', 'bibtex']"
zaccharieramzi/XPDNet-brain-af8,zaccharieramzi,"['arxiv.org/abs/2010.07290', 'bibtex']"
NimaBoscarino/ventricular_short_axis_3label,NimaBoscarino,['doi.org/10.1007/978-3-030-12029-0_40`']
NimaBoscarino/unicorn_track_large_mot_challenge_mask,NimaBoscarino,"['arxiv.org/abs/2111.12085', {'title': 'Towards Grand Unification of Object Tracking'}]"
Axon/resnet50-v1,Axon,['arxiv.org/abs/1512.03385']
Axon/resnet18-v1,Axon,['arxiv.org/abs/1512.03385']
Nano1337/SpecLab,Nano1337,['arxiv.org/abs/1910.09700']
Axon/resnet34-v1,Axon,['arxiv.org/abs/1512.03385']
Chris1/mutant-ape-yacht-club__2__boredapeyachtclub,Chris1,['bibtex']
Chris1/real2sim-512,Chris1,['bibtex']
NimaBoscarino/efficientformer-l7-300,NimaBoscarino,"['arxiv.org/abs/2206.01191', {'title': 'EfficientFormer: Vision Transformers at MobileNet Speed'}]"
NimaBoscarino/efficientformer-l3-300,NimaBoscarino,"['arxiv.org/abs/2206.01191', {'title': 'EfficientFormer: Vision Transformers at MobileNet Speed'}]"
Chris1/CycleGAN_punk2apes,Chris1,['bibtex']
NimaBoscarino/DiffusionCLIP-CelebA_HQ,NimaBoscarino,"['arxiv.org/abs/1710.10196', {'title': 'Diffusionclip: Text-guided image manipulation using diffusion models'}]"
ChristianOrr/madnet_keras,ChristianOrr,"['arxiv.org/abs/1810.05424', 'bibtex']"
Chris1/real2sim,Chris1,['bibtex']
Chris1/ape2punk_epoch80,Chris1,['bibtex']
NimaBoscarino/DiffusionCLIP-LSUN_Bedroom,NimaBoscarino,[{'title': 'Diffusionclip: Text-guided image manipulation using diffusion models'}]
Chris1/sim2real-512,Chris1,['bibtex']
Coolhand/Abuela,Coolhand,[{'title': 'Bringing Old Photos Back to Life'}]
CompVis/stable-diffusion,CompVis,['arxiv.org/abs/2112.10752']
scikit-learn/skops-blog-example,scikit-learn,['bibtex']
MultiBertGunjanPatrick/multiberts-seed-1-20k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
yogeshkulkarni/MidcurveNN,yogeshkulkarni,"['doi.org/10.14733/cadaps.2022.1154-1161},', {'doi': 'https://doi.org/10.14733/cadaps.2022.1154-1161'}]"
MultiBertGunjanPatrick/multiberts-seed-1-40k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"
NAACL2022/cogmen,NAACL2022,"['arxiv.org/abs/2205.02455', 'bibtex']"
sberbank-ai/Real-ESRGAN,sberbank-ai,['arxiv.org/abs/2107.10833']
MultiBertGunjanPatrick/multiberts-seed-2-40k,MultiBertGunjanPatrick,"['arxiv.org/abs/2106.16163', 'bibtex']"