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Cainiao-AI
/
TAAS

Feature Extraction
Transformers
PyTorch
Chinese
TAAS
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Cainiao-AI/TAAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Cainiao-AI/TAAS with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Cainiao-AI/TAAS", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Cainiao-AI/TAAS", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
TAAS
1.69 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
zy414775's picture
zy414775
Update configuration_TAAS.py
e72a9a2 almost 3 years ago
  • imgs
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  • .gitattributes
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  • INIT.sh
    1.46 kB
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  • README.md
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  • TAAS_utils.py
    54.6 kB
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  • chn_2_code.pkl
    6.04 MB
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  • config.json
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  • configuration_TAAS.py
    1.61 kB
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  • graphormer.py
    17.8 kB
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  • htc_loss.py
    6.84 kB
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  • htc_mask_dict_old.pkl
    251 kB
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  • modeling_TAAS.py
    49.8 kB
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  • ner_model.py
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  • pytorch_model.bin
    1.67 GB
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  • remap_code_2_chn.pkl
    4.16 MB
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  • remap_code_2_chn_with_all_htc.pkl
    6.24 MB
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  • requirements.txt
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  • special_tokens_map.json
    125 Bytes
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  • tokenizer_config.json
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  • utils.py
    4.84 kB
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  • vocab.txt
    187 kB
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