Instructions to use SkillScreen/SkillScreen-2B-SBR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillScreen/SkillScreen-2B-SBR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-SBR")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-SBR") model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-SBR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scoring_config.json from SkillScreen/SkillScreen-2B-SBR: direct link, hf CLI and curl.
- Browser
- Download file 207 Bytes
-
https://huggingface.co/SkillScreen/SkillScreen-2B-SBR/resolve/main/scoring_config.json
- Command line
-
hf download hf://SkillScreen/SkillScreen-2B-SBR/scoring_config.json
-
curl -L -o scoring_config.json https://huggingface.co/SkillScreen/SkillScreen-2B-SBR/resolve/main/scoring_config.json
207 Bytes
| { | |
| "input": { | |
| "scope": "SKILL.md only", | |
| "hypothesis": "This AI agent skill is malicious.", | |
| "max_length": 8192 | |
| }, | |
| "score": "native entailment softmax", | |
| "decision_threshold": 0.5 | |
| } | |