Token Classification
Transformers
Safetensors
English
modernbert
security
jailbreak-detection
prompt-injection
tool-calling
llm-safety
mcp
Eval Results (legacy)
Instructions to use rootfs/tool-call-verifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootfs/tool-call-verifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rootfs/tool-call-verifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rootfs/tool-call-verifier") model = AutoModelForTokenClassification.from_pretrained("rootfs/tool-call-verifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,020 Bytes
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"classification_report": {
"AUTHORIZED": {
"precision": 0.9037877897531266,
"recall": 0.9392273925756695,
"f1-score": 0.9211668545659526,
"support": 139191.0
},
"UNAUTHORIZED": {
"precision": 0.950093511979563,
"recall": 0.9204538309851105,
"f1-score": 0.9350388443092216,
"support": 174955.0
},
"accuracy": 0.9287719722676717,
"macro avg": {
"precision": 0.9269406508663448,
"recall": 0.9298406117803899,
"f1-score": 0.9281028494375871,
"support": 314146.0
},
"weighted avg": {
"precision": 0.9295764919238567,
"recall": 0.9287719722676717,
"f1-score": 0.9288924788474447,
"support": 314146.0
}
},
"accuracy": 0.9287719722676717,
"macro_f1": 0.9281028494375871,
"weighted_f1": 0.9288924788474447,
"unauthorized_avg_f1": 0.9350388443092216,
"unauthorized_precision": 0.950093511979563,
"unauthorized_recall": 0.9204538309851105,
"unauthorized_f1": 0.9350388443092216
} |