Text Classification
Transformers
Safetensors
English
lfm2_vl
feature-extraction
decision-model
typed-decisions
system-one
shell
guardrail
command-risk
lfm2
liquid
lora
custom_code
Eval Results (legacy)
Instructions to use yannelli/d1-3b-cli-command-risk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yannelli/d1-3b-cli-command-risk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yannelli/d1-3b-cli-command-risk", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("yannelli/d1-3b-cli-command-risk", trust_remote_code=True) model = AutoModel.from_pretrained("yannelli/d1-3b-cli-command-risk", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: drop a repeated sentence
Browse files
README.md
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## Results
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Results cover all 12,689 rows of the `test` split: 97 template families and 2,551 distinct commands, each in up to five shells.
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| Model | Macro F1 @ 0.5 | Level macro F1 | Accuracy | Log loss | Brier | ECE | Safe judged safe |
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## Results
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Results cover all 12,689 rows of the `test` split: 97 template families and 2,551 distinct commands, each in up to five shells. Each command was one request with all 17 questions, scored with `training/score_cli_decisions.py`, which computes the same columns as the scorer of [JS/TS Code Review Decisions](https://huggingface.co/datasets/yannelli/js-ts-code-review-decisions).
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| Model | Macro F1 @ 0.5 | Level macro F1 | Accuracy | Log loss | Brier | ECE | Safe judged safe |
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