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improved_stance_detection_v2-fold2
aomar85/improved_stance_detection_v2-fold2
improved_stance_detection_v2-fold2 is a text classification model from aomar85. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Feb 15, 2026
- Updated
- Feb 15, 2026
- SHA
- 8d95671c
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
improved_stance_detection_v2-fold2
This model is a fine-tuned version of aubmindlab/bert-large-arabertv02-twitter on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2078
- Accuracy: 0.8098
- Macro F1: 0.8108
- Weighted F1: 0.8124
- F1 Pro: 0.8372
- F1 Against: 0.8261
- F1 Neutral: 0.7692
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral |
|---|---|---|---|---|---|---|---|---|---|
| 2.0893 | 1.9320 | 50 | 0.4121 | 0.5073 | 0.4822 | 0.4776 | 0.5714 | 0.3093 | 0.5658 |
| 1.5103 | 3.8544 | 100 | 0.3107 | 0.6878 | 0.6886 | 0.6910 | 0.7438 | 0.7006 | 0.6212 |
| 1.0102 | 5.7767 | 150 | 0.2688 | 0.7220 | 0.7252 | 0.7273 | 0.7692 | 0.7397 | 0.6667 |
| 0.8139 | 7.6990 | 200 | 0.2152 | 0.7951 | 0.7965 | 0.7976 | 0.8254 | 0.7971 | 0.7671 |
| 0.6407 | 9.6214 | 250 | 0.2380 | 0.7951 | 0.7972 | 0.7980 | 0.8293 | 0.7910 | 0.7712 |
| 0.5584 | 11.5437 | 300 | 0.2076 | 0.8098 | 0.8108 | 0.8124 | 0.8372 | 0.8261 | 0.7692 |
| 0.4945 | 13.4660 | 350 | 0.2123 | 0.7951 | 0.7969 | 0.7986 | 0.8413 | 0.8028 | 0.7465 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Feb 15, 2026
- Updated
- Feb 15, 2026