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mpalinski
/
ConTeXT-ESCO-Matcher

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use mpalinski/ConTeXT-ESCO-Matcher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use mpalinski/ConTeXT-ESCO-Matcher with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("mpalinski/ConTeXT-ESCO-Matcher")
    
    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]
  • Notebooks
  • Google Colab
  • Kaggle
ConTeXT-ESCO-Matcher
462 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
mpalinski's picture
mpalinski
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f8291b5 verified 3 months ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    3.78 kB
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  • config.json
    706 Bytes
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  • config_sentence_transformers.json
    212 Bytes
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  • esco_data.pt

    Detected Pickle imports (3)

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

    What is a pickle import?

    23 MB
    xet
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  • handler.py
    4.47 kB
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  • model.safetensors
    438 MB
    xet
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  • modules.json
    242 Bytes
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  • requirements.txt
    22 Bytes
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  • sentence_bert_config.json
    56 Bytes
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  • special_tokens_map.json
    1.02 kB
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  • tokenizer.json
    711 kB
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  • tokenizer_config.json
    1.71 kB
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  • vocab.txt
    232 kB
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