Text Classification
Transformers
Safetensors
English
qwen3_5
image-text-to-text
decision-model
classification
calibration
routing
guardrails
vision
multimodal
baseten
Eval Results
Instructions to use baselabs/joey-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baselabs/joey-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baselabs/joey-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("baselabs/joey-1") model = AutoModelForMultimodalLM.from_pretrained("baselabs/joey-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add safeagents judge bench results
#1
by ybelkada HF Staff - opened
Hi! This adds baselabs/joey-1's results on the safeagents judge bench, a benchmark of real agent tool calls that safeagents' scanner flags as suspicious, each labelled block or allow and with a kind of misuse (reward hacking, harness introspection, credential misuse, ...).
On the 562 test calls: balanced accuracy 0.502, AUROC 0.482, false-block rate 0.154, category macro F1 0.219. Run zero-shot, one System One request per call, on NVIDIA L4 (HF Jobs). Per-call verdicts are in the dataset's results/ folder.
The file is named after the benchmark, so it doesn't touch any existing .eval_results file.