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imaneb942
/
MNLP_M3_document_encoder

Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
OpenVINO
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use imaneb942/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use imaneb942/MNLP_M3_document_encoder with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="imaneb942/MNLP_M3_document_encoder")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("imaneb942/MNLP_M3_document_encoder")
    model = AutoModel.from_pretrained("imaneb942/MNLP_M3_document_encoder")
  • Notebooks
  • Google Colab
  • Kaggle
MNLP_M3_document_encoder / onnx
2.34 GB
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  • 1 contributor
History: 1 commit
imaneb942's picture
imaneb942
Upload 8 files
a1771cd verified 11 months ago
  • config.json
    657 Bytes
    Upload 8 files 11 months ago
  • model.onnx
    1.34 GB
    xet
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  • model_O4.onnx
    668 MB
    xet
    Upload 8 files 11 months ago
  • model_qint8_avx512_vnni.onnx
    337 MB
    xet
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  • special_tokens_map.json
    132 Bytes
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  • tokenizer.json
    742 kB
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  • tokenizer_config.json
    355 Bytes
    Upload 8 files 11 months ago
  • vocab.txt
    262 kB
    Upload 8 files 11 months ago