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binqiangliu
/
EmbeddingModelallMiniLML6v2

Feature Extraction
Transformers
PyTorch
google-tensorflow TensorFlow
Rust
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use binqiangliu/EmbeddingModelallMiniLML6v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use binqiangliu/EmbeddingModelallMiniLML6v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="binqiangliu/EmbeddingModelallMiniLML6v2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("binqiangliu/EmbeddingModelallMiniLML6v2")
    model = AutoModel.from_pretrained("binqiangliu/EmbeddingModelallMiniLML6v2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
EmbeddingModelallMiniLML6v2
274 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 15 commits
binqiangliu's picture
binqiangliu
Upload config.json
c4ffd6b over 2 years ago
  • 1_Pooling
    Upload config.json over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • config.json
    612 Bytes
    Upload config.json with huggingface_hub over 2 years ago
  • config_sentence_transformers.json
    116 Bytes
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  • data_config.json
    39.3 kB
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  • modules.json
    349 Bytes
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  • pytorch_model.bin
    90.9 MB
    xet
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  • rust_model.ot
    90.9 MB
    xet
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    112 Bytes
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  • tf_model.h5
    91 MB
    xet
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  • tokenizer.json
    466 kB
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  • tokenizer_config.json
    350 Bytes
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  • train_script.py
    13.2 kB
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  • vocab.txt
    232 kB
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