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llm-semantic-router
/
toolcall-verifier

Token Classification
Transformers
Safetensors
English
modernbert
security
jailbreak-detection
prompt-injection
tool-calling
llm-safety
mcp
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use llm-semantic-router/toolcall-verifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use llm-semantic-router/toolcall-verifier with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="llm-semantic-router/toolcall-verifier")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/toolcall-verifier")
    model = AutoModelForTokenClassification.from_pretrained("llm-semantic-router/toolcall-verifier")
  • Notebooks
  • Google Colab
  • Kaggle
toolcall-verifier
602 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
Xunzhuo's picture
Xunzhuo
Update README.md
48c3fec verified 5 months ago
  • .gitattributes
    1.52 kB
    initial commit 5 months ago
  • README.md
    4.52 kB
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  • best_metrics.json
    1.02 kB
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  • config.json
    1.36 kB
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  • final_report.json
    248 Bytes
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  • model.safetensors
    598 MB
    xet
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  • special_tokens_map.json
    694 Bytes
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  • tokenizer.json
    3.58 MB
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  • tokenizer_config.json
    20.8 kB
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  • training_config.json
    481 Bytes
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