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NeuML
/
bert-hash-pico-embeddings

Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert_hash
feature-extraction
custom_code
Model card Files Files and versions
xet
Community

Instructions to use NeuML/bert-hash-pico-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use NeuML/bert-hash-pico-embeddings with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("NeuML/bert-hash-pico-embeddings", trust_remote_code=True)
    
    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 NeuML/bert-hash-pico-embeddings with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("NeuML/bert-hash-pico-embeddings", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
bert-hash-pico-embeddings
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  • 1 contributor
History: 4 commits
davidmezzetti's picture
davidmezzetti
Add Transformers v5 support
0083b5a 9 days ago
  • 1_Pooling
    Upload model 4 months ago
  • .gitattributes
    1.52 kB
    initial commit 4 months ago
  • README.md
    7.21 kB
    Update README.md 19 days ago
  • config.json
    903 Bytes
    Upload model 4 months ago
  • config_sentence_transformers.json
    283 Bytes
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  • configuration_bert_hash.py
    340 Bytes
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  • model.safetensors
    1.8 MB
    xet
    Upload model 4 months ago
  • modeling_bert_hash.py
    17.4 kB
    Add Transformers v5 support 9 days ago
  • modules.json
    229 Bytes
    Upload model 4 months ago
  • sentence_bert_config.json
    57 Bytes
    Upload model 4 months ago
  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.41 kB
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  • vocab.txt
    232 kB
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