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fangyuan
/
tqa_extractive_compressor

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

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

  • Libraries
  • Transformers

    How to use fangyuan/tqa_extractive_compressor with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="fangyuan/tqa_extractive_compressor")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("fangyuan/tqa_extractive_compressor")
    model = AutoModel.from_pretrained("fangyuan/tqa_extractive_compressor")
  • Notebooks
  • Google Colab
  • Kaggle
tqa_extractive_compressor
439 MB
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  • 1 contributor
History: 3 commits
fangyuan's picture
fangyuan
Delete tqa_test_top5_sent_result.json
2c2e858 verified about 2 years ago
  • 1_Pooling
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  • .gitattributes
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  • config.json
    668 Bytes
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  • config_sentence_transformers.json
    124 Bytes
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  • modules.json
    229 Bytes
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  • pytorch_model.bin
    438 MB
    xet
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  • sentence_bert_config.json
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  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    358 Bytes
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  • vocab.txt
    232 kB
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