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: 518 Bytes
7e9cfd1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # target descriptions on for cross-target grounding; check the aug booster
# separately once we have a baseline dev score
model_hf: aubmindlab/bert-base-arabertv02-twitter
train_csv: data/track2/train.csv
dev_csv: data/track2/dev.csv
out_dir: outputs/t2_arabert
seed: 42
max_len: 128
batch_size: 16
eval_batch_size: 64
lr: 2.0e-5
weight_decay: 0.01
epochs: 12
patience: 3
loss: ce
focal_gamma: 2.0
prep_mode: preserve
use_description: true # short target description helps cross-target generalization
none_bias: 0.0
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