Feature Extraction
PEFT
embeddings
text-classification
distillation
knowledge-distillation
generated-code
status:scaffold
Instructions to use Kodep/jev-topic-head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Kodep/jev-topic-head with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 761 Bytes
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Raw outputs so we can recompute the headline scores **without re-running the model**.
## Files to push
| file | what it is |
|---|---|
| `per_item_test.jsonl` | one line per test item: model topic, Jev topic, confidence, show/hide for each |
| `confusion.json` | 56×56 confusion matrix over the test split |
## Suggested schema for `per_item_test.jsonl`
```json
{"id": "...", "model_topic": "...", "jev_topic": "...", "confidence": 0.71, "model_show_hide": "show", "jev_show_hide": "show", "human_topic": null}
```
## Numbers to reproduce from this
- top-1 topic accuracy vs Jev (report: 92/105 = 87.6%)
- macro-F1 (report: 0.572)
- show/hide agreement (report: 95/105 = 90.5%)
- confidence split: of 24 errors, 19 < 0.5 confidence, only 1 ≥ 0.9
|