Instructions to use judithrosell/PubMedBERT_BioNLP13CG_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use judithrosell/PubMedBERT_BioNLP13CG_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="judithrosell/PubMedBERT_BioNLP13CG_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("judithrosell/PubMedBERT_BioNLP13CG_NER") model = AutoModelForTokenClassification.from_pretrained("judithrosell/PubMedBERT_BioNLP13CG_NER") - Notebooks
- Google Colab
- Kaggle
Commit ·
883cca0
1
Parent(s): 0af77ae
Training in progress, epoch 0
Browse files- config.json +95 -0
- model.safetensors +3 -0
- runs/Dec23_17-46-33_fae125109bef/events.out.tfevents.1703353594.fae125109bef.559.0 +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
config.json
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{
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"_name_or_path": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "B-Amino_acid",
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"1": "B-Anatomical_system",
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"2": "B-Cancer",
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"3": "B-Cell",
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"4": "B-Cellular_component",
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"5": "B-Developing_anatomical_structure",
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"6": "B-Gene_or_gene_product",
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"7": "B-Immaterial_anatomical_entity",
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"8": "B-Multi-tissue_structure",
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"9": "B-Organ",
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"10": "B-Organism",
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"11": "B-Organism_subdivision",
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"12": "B-Organism_substance",
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"13": "B-Pathological_formation",
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"14": "B-Simple_chemical",
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"15": "B-Tissue",
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"16": "I-Amino_acid",
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"17": "I-Anatomical_system",
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"18": "I-Cancer",
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"19": "I-Cell",
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"20": "I-Cellular_component",
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"21": "I-Developing_anatomical_structure",
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"22": "I-Gene_or_gene_product",
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"23": "I-Immaterial_anatomical_entity",
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"24": "I-Multi-tissue_structure",
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"25": "I-Organ",
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"26": "I-Organism",
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"27": "I-Organism_subdivision",
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"28": "I-Organism_substance",
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"29": "I-Pathological_formation",
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"30": "I-Simple_chemical",
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"31": "I-Tissue",
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"32": "O"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-Amino_acid": 0,
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"B-Anatomical_system": 1,
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"B-Cancer": 2,
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"B-Cell": 3,
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"B-Cellular_component": 4,
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"B-Developing_anatomical_structure": 5,
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"B-Gene_or_gene_product": 6,
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"B-Immaterial_anatomical_entity": 7,
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"B-Multi-tissue_structure": 8,
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"B-Organ": 9,
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"B-Organism": 10,
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"B-Organism_subdivision": 11,
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"B-Organism_substance": 12,
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"B-Pathological_formation": 13,
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"B-Simple_chemical": 14,
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"B-Tissue": 15,
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"I-Amino_acid": 16,
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"I-Anatomical_system": 17,
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"I-Cancer": 18,
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"I-Cell": 19,
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"I-Cellular_component": 20,
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"I-Developing_anatomical_structure": 21,
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"I-Gene_or_gene_product": 22,
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"I-Immaterial_anatomical_entity": 23,
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"I-Multi-tissue_structure": 24,
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"I-Organ": 25,
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"I-Organism": 26,
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"I-Organism_subdivision": 27,
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"I-Organism_substance": 28,
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"I-Pathological_formation": 29,
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"I-Simple_chemical": 30,
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"I-Tissue": 31,
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"O": 32
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:23a1a897f2dcc99d5e87b18621a3956a9c359dbf57011555f44f3333ea07c1d1
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size 435691444
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runs/Dec23_17-46-33_fae125109bef/events.out.tfevents.1703353594.fae125109bef.559.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d7f34950a1cfdd25c4d4f8f9c1befadeac7a1ee854916d4c233d37c69477fa7
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size 6712
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:ed2988ebc47e6953943d266f1a66655f61b3ba1ff4c6904e2c206021e72c4739
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size 4600
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vocab.txt
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