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BSC-NLP4BIA
/
location-sub-classifier

Text Classification
sentence-transformers
PyTorch
setfit
Spanish
roberta
biomedical
clinical
EHR
spanish
location
birth place
residence
movement
medical care
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use BSC-NLP4BIA/location-sub-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use BSC-NLP4BIA/location-sub-classifier with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("BSC-NLP4BIA/location-sub-classifier")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • setfit

    How to use BSC-NLP4BIA/location-sub-classifier with setfit:

    from setfit import SetFitModel
    
    model = SetFitModel.from_pretrained("BSC-NLP4BIA/location-sub-classifier")
  • Notebooks
  • Google Colab
  • Kaggle
location-sub-classifier
1.01 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 6 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
9106de1 verified over 1 year ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    5.24 kB
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  • config.json
    749 Bytes
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  • config_sentence_transformers.json
    123 Bytes
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  • merges.txt
    540 kB
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  • model.safetensors
    504 MB
    xet
    Adding `safetensors` variant of this model over 1 year ago
  • model_head.pkl
    33.4 kB
    xet
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  • modules.json
    229 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    504 MB
    xet
    Upload 12 files over 1 year ago
  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    957 Bytes
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  • tokenizer.json
    2.32 MB
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  • tokenizer_config.json
    1.38 kB
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  • vocab.json
    894 kB
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