Token Classification
Transformers
Safetensors
vela
semantic-router
decision-model
ai-agents
agent-security
prompt-injection
secret-detection
web3
guardrails
Instructions to use VelaOrg/vela-agent-guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VelaOrg/vela-agent-guard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="VelaOrg/vela-agent-guard")# pip install -U transformers accelerate # Load model directly from transformers import VelaAgentGuardHeads model = VelaAgentGuardHeads.from_pretrained("VelaOrg/vela-agent-guard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scripts/build_eval.py from VelaOrg/vela-agent-guard: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/VelaOrg/vela-agent-guard/resolve/main/scripts/build_eval.py
- Command line
-
hf download hf://VelaOrg/vela-agent-guard/scripts/build_eval.py
-
curl -L -o build_eval.py https://huggingface.co/VelaOrg/vela-agent-guard/resolve/main/scripts/build_eval.py
1.63 kB
| """Write synthetic data: data/<name>/{secrets,injection,toolcall}.jsonl. | |
| python scripts/build_eval.py # eval set (eval templates) | |
| python scripts/build_eval.py --split train --n 4000 --seed 1 --out data/train | |
| python scripts/build_eval.py --split train --n 400 --seed 2 --out data/dev | |
| """ | |
| import argparse | |
| import json | |
| import logging | |
| import random | |
| from pathlib import Path | |
| from vela_guard import gen_injection, gen_secrets, gen_toolcall | |
| GENERATORS = {"secrets": gen_secrets, "injection": gen_injection, "toolcall": gen_toolcall} | |
| EVAL_SEED = 20261012 | |
| log = logging.getLogger("build_eval") | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) | |
| parser.add_argument("--out", type=Path, default=Path("data/eval")) | |
| parser.add_argument("--n", type=int, default=300, help="examples per task") | |
| parser.add_argument("--seed", type=int, default=EVAL_SEED) | |
| parser.add_argument("--split", choices=("eval", "train"), default="eval") | |
| args = parser.parse_args() | |
| args.out.mkdir(parents=True, exist_ok=True) | |
| for offset, (task, module) in enumerate(GENERATORS.items()): | |
| rows = module.generate(random.Random(args.seed + offset), args.n, split=args.split) | |
| path = args.out / f"{task}.jsonl" | |
| path.write_text("".join(json.dumps(r, ensure_ascii=False) + "\n" for r in rows)) | |
| log.info("%s: %d rows (%d hard) -> %s", task, len(rows), sum(r["hard"] for r in rows), path) | |
| if __name__ == "__main__": | |
| logging.basicConfig(level=logging.INFO, format="%(message)s") | |
| main() | |