Sentence Similarity
Transformers
Safetensors
multilingual
nllb-llm2vec
image-feature-extraction
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
text-reranking
feature-extraction
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
custom_code
Instructions to use fdschmidt93/NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fdschmidt93/NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fdschmidt93/NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0202f54e596c03be98c5a537e9214967476071dce868ac20a7a42ad57fdd1f7b
- Size of remote file:
- 17.3 MB
- SHA256:
- e316b82de11d0f951f370943b3c438311629547285129b0b81dadabd01bca665
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