Text Classification
Transformers
Safetensors
Arabic
Stance Detection
Text Classification
arabic-nlp
stanceeval-2026
few-shot-learning
retrieval-augmented
Mawqif-v2
ensemble
LoRA
AraBERT
MARBERT
Instructions to use zaher-m/stanceeval2026 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zaher-m/stanceeval2026 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zaher-m/stanceeval2026")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zaher-m/stanceeval2026", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,136 Bytes
7e9cfd1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Generated data
Training sets we generated or augmented for the released models. Each row is `text, target, stance`
with `stance` in `{Favor, Against, None}`. Paths match what the configs and training scripts expect,
so run them with this folder as the data root.
| File | Rows | How it was made | Used by |
|---|---|---|---|
| `track2/train_aug.csv` | 8,056 | Track-2 train plus LLM paraphrases; a `source` column marks `original` (2,714) or `paraphrase` (5,342) | encoder `t2_marbert_aug`, via `configs/track2_aug.yaml` |
| `synth/t1_train_style.csv` | 3,902 | Track-1 train plus style-grounded generated shots | LoRA `allam_style` |
| `external/pool_real_all.csv` | 4,449 | pooled real-target shots (Women Empowerment, Covid, Digital) | LoRA `allam_real` |
The official Mawqif-v2 splits belong to the organizers and are not redistributed here. Get them from
the shared task at https://stanceeval.github.io/. Everything above is derived from those splits and
can be regenerated:
```bash
python -m src.gen_synth --help # style and target-matched shot pools
python -m src.augment --help # paraphrase augmentation
```
|