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: 1,234 Bytes
4e75514 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # src/
The training + evaluation code. **This is the primary thing we want to read** — it's the only way to confirm what the Claude-written report actually did.
## Files to push
| file | what it is |
|---|---|
| `train_head.py` | LoRA + prototype-head training |
| `evaluate.py` | metrics: top-1 accuracy, macro-F1, show/hide, confidence buckets |
| `rules_table.json` | the fixed step-4 show/hide rules (no AI) |
| `config.yaml` | all hyperparameters (see the checklist in the root README) |
## Also include so it runs offline
- `requirements.txt` (or `environment.yml`) — torch version, bf16 support, `transformers`/`peft` versions
- a short **how-to-run**: base model path (`Qwen/Qwen3-Embedding-0.6B`), seed flag, config flag, output paths
- any preprocessing that turns a raw item into the model input (concatenation order, tokenization, max length)
## What we'll check in `train_head.py` (vs the root README checklist)
LoRA rank/alpha/dropout + which modules · prototype head + cosine×scale + scale init 20 · prototype seeding from description embeddings · loss + label smoothing · the two AdamW groups + warmup/decay · early-stop on val macro-F1 · class-balanced sampling + hard-pair weighting · seed handling.
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