Token Classification
Transformers
Safetensors
English
modernbert
security
jailbreak-detection
prompt-injection
tool-calling
llm-safety
mcp
Eval Results (legacy)
Instructions to use vllm-sr/toolcall-verifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/toolcall-verifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vllm-sr/toolcall-verifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/toolcall-verifier") model = AutoModelForTokenClassification.from_pretrained("vllm-sr/toolcall-verifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download final_report.json from vllm-sr/toolcall-verifier: direct link, hf CLI and curl.
- Browser
- Download file 248 Bytes
-
https://huggingface.co/vllm-sr/toolcall-verifier/resolve/main/final_report.json
- Command line
-
hf download hf://vllm-sr/toolcall-verifier/final_report.json
-
curl -L -o final_report.json https://huggingface.co/vllm-sr/toolcall-verifier/resolve/main/final_report.json
248 Bytes
| { | |
| "accuracy": 0.9287719722676717, | |
| "unauthorized_precision": 0.950093511979563, | |
| "unauthorized_recall": 0.9204538309851105, | |
| "unauthorized_f1": 0.9350388443092216, | |
| "unauthorized_avg_f1": 0.9350388443092216, | |
| "macro_f1": 0.9281028494375871 | |
| } |