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QA-DeBERTa-v3-large-binary-focal
saiteki-kai/QA-DeBERTa-v3-large-binary-focal
QA-DeBERTa-v3-large-binary-focal is a text classification model from saiteki-kai. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Nov 17, 2025
- Updated
- Nov 18, 2025
- SHA
- 77d51302
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Base models
Datasets
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. -->
QA-DeBERTa-v3-large-binary-focal
This model is a fine-tuned version of microsoft/deberta-v3-large on the saiteki-kai/Beavertails-it dataset. It achieves the following results on the evaluation set:
- Loss: 0.0603
- Accuracy: 0.8625
- Unsafe Precision: 0.8786
- Unsafe Recall: 0.8735
- Unsafe F1: 0.8761
- Unsafe Fpr: 0.1514
- Unsafe Aucpr: 0.9564
- Safe Precision: 0.8425
- Safe Recall: 0.8486
- Safe F1: 0.8455
- Safe Fpr: 0.1265
- Safe Aucpr: 0.9242
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: 9e-06
- train_batch_size: 64
- eval_batch_size: 512
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Unsafe Precision | Unsafe Recall | Unsafe F1 | Unsafe Fpr | Unsafe Aucpr | Safe Precision | Safe Recall | Safe F1 | Safe Fpr | Safe Aucpr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0644 | 1.0 | 8454 | 0.0623 | 0.8604 | 0.8730 | 0.8766 | 0.8748 | 0.1600 | 0.9540 | 0.8444 | 0.8400 | 0.8422 | 0.1234 | 0.9189 |
| 0.0534 | 2.0 | 16908 | 0.0603 | 0.8625 | 0.8786 | 0.8736 | 0.8761 | 0.1515 | 0.9564 | 0.8425 | 0.8485 | 0.8455 | 0.1264 | 0.9242 |
| 0.0535 | 3.0 | 25362 | 0.0619 | 0.8615 | 0.8774 | 0.8730 | 0.8752 | 0.1530 | 0.9564 | 0.8417 | 0.8470 | 0.8443 | 0.1270 | 0.9252 |
| 0.0502 | 4.0 | 33816 | 0.0679 | 0.8584 | 0.8676 | 0.8797 | 0.8736 | 0.1684 | 0.9554 | 0.8464 | 0.8316 | 0.8389 | 0.1203 | 0.9252 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.7.1+cu118
- Datasets 4.4.1
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Nov 17, 2025
- Updated
- Nov 18, 2025