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InfiniFlow
/
bce-embedding-base_v1

Feature Extraction
Transformers
PyTorch
xlm-roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use InfiniFlow/bce-embedding-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use InfiniFlow/bce-embedding-base_v1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="InfiniFlow/bce-embedding-base_v1")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("InfiniFlow/bce-embedding-base_v1")
    model = AutoModel.from_pretrained("InfiniFlow/bce-embedding-base_v1")
  • Notebooks
  • Google Colab
  • Kaggle
bce-embedding-base_v1
1.13 GB
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  • 1 contributor
History: 2 commits
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HuParser
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00a7db2 verified about 2 years ago
  • 1_Pooling
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  • .gitattributes
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  • README.md
    28 Bytes
    initial commit about 2 years ago
  • config.json
    741 Bytes
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  • config_sentence_transformers.json
    117 Bytes
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  • modules.json
    349 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    1.11 GB
    xet
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  • sentence_bert_config.json
    53 Bytes
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  • sentencepiece.bpe.model
    5.07 MB
    xet
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  • special_tokens_map.json
    280 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    1.17 kB
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