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The pipeline that produces predictions from the released models, for StanceEval-2026 Track 1 (seen
targets) and Track 2 (unseen targets). Base LLMs are fetched from their own Hugging Face repos.
```bash
pip install -r ../requirements.txt
```
## Prediction
1. Encoder ensemble, averaged softmax over the fine-tuned encoders:
```bash
python -m src.predict --models <MODEL_DIRS> --csv <test.csv> --out preds_enc.txt
```
2. Retrieval few-shot LLM, with MARBERTv2-retrieved shots over a served instruction model on an
OpenAI-compatible endpoint:
```bash
export AUG_BASE_URL="http://localhost:8017/v1"; export AUG_MODEL="LilaRest/gemma-4-31B-it-NVFP4-turbo"
python -m src.llm_classify --csv <test.csv> --train <pool.csv> --retrieve \
--embed_model UBC-NLP/MARBERTv2 --shots 6 --n 6 --mode direct \
--out_probs gemma.npy --base_url "$AUG_BASE_URL" --model "$AUG_MODEL"
```
3. LoRA member, scored by label log-probability:
```bash
python -m src.llm_infer --adapter <lora_dir> --base_model ALLaM-AI/ALLaM-7B-Instruct-preview \
--csv <test.csv> --out_probs allam.npy
```
4. Blend and None calibration, then labels:
```bash
python -m src.blend_tune --csv <test.csv> --models <MODEL_DIRS> --enc_weight 0.45 \
--llm_probs gemma.npy allam.npy --llm_weights 0.5 0.5 --none_bias 0.0 --out pred.txt
```
The final label decision uses the plug-in rule for *F*<sub>avg2</sub>: claim class *c* when *P*(*c*)
exceeds *F*<sub>c</sub>/2, otherwise fall back to `None`.
## Training
Encoders are config-driven, and the full config also lands in each model's `best.json`:
```bash
python -m src.train --config configs/track{1,2}.yaml --overrides model_hf=<id>,out_dir=<dir>
python -m src.train --config configs/track2_aug.yaml # uses data/track2/train_aug.csv
```
LoRA adapters (r=16, α=32, base fetched from HF):
```bash
python -m src.llm_finetune --train_csv <train.csv> \
--base_model ALLaM-AI/ALLaM-7B-Instruct-preview --out_dir outputs/allam_t2 \
--epochs 3 --batch_size 16 --save_every 15
```
The generated pools in `../data/` come from `src/gen_synth.py` (style and target-matched shots) and
`src/augment.py` (paraphrase augmentation). See [`../data/README.md`](../data/README.md).
## Rebuilding an auxiliary encoder
Four encoders were used only as probability sources and never saved. Override the base id to rebuild
them, for example AraELECTRA:
```bash
python -m src.train --config configs/track1.yaml \
--overrides model_hf=aubmindlab/araelectra-base-discriminator,out_dir=outputs/t1_araelectra
```
The same pattern rebuilds the XLM-R-large, ARBERTv2 and AraBERT-large members. Encoder label order is
`["Against","Favor","None"]`, set in `src/data.py`.
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