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QCRI
/
OmniScore-mxbai

Feature Extraction
Transformers
Safetensors
score_predictor
mxbai
regression
text-evaluation
multilingual
trust-remote-code
custom_code
Model card Files Files and versions
xet
Community

Instructions to use QCRI/OmniScore-mxbai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use QCRI/OmniScore-mxbai with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="QCRI/OmniScore-mxbai", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("QCRI/OmniScore-mxbai", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
OmniScore-mxbai
1.47 GB
Ctrl+K
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  • 1 contributor
History: 4 commits
gagan3012's picture
gagan3012
Update modeling_score_predictor.py
9608763 verified 4 months ago
  • .gitattributes
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  • README.md
    6.21 kB
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  • __init__.py
    307 Bytes
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  • config.json
    724 Bytes
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  • configuration_score_predictor.py
    2.43 kB
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  • explain_score_predictor.py
    36.1 kB
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  • metrics_final.json
    3.21 kB
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  • model.safetensors
    1.36 GB
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  • modeling_score_predictor.py
    24.3 kB
    Update modeling_score_predictor.py 4 months ago
  • predictions.jsonl
    99.3 MB
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    711 kB
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  • tokenizer_config.json
    1.27 kB
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  • vocab.txt
    232 kB
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