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gubartz
/
st_scibert_abstruct

Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use gubartz/st_scibert_abstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use gubartz/st_scibert_abstruct with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("gubartz/st_scibert_abstruct")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Transformers

    How to use gubartz/st_scibert_abstruct with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("gubartz/st_scibert_abstruct")
    model = AutoModel.from_pretrained("gubartz/st_scibert_abstruct")
  • Notebooks
  • Google Colab
  • Kaggle
st_scibert_abstruct
441 MB
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  • 1 contributor
History: 12 commits
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  • .gitattributes
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  • README.md
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  • config.json
    638 Bytes
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  • config_sentence_transformers.json
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  • modules.json
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  • pytorch_model.bin
    440 MB
    xet
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    125 Bytes
    Pushing model almost 3 years ago
  • tokenizer.json
    717 kB
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  • tokenizer_config.json
    388 Bytes
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  • vocab.txt
    228 kB
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