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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- single_label_classification
- question-answering
- text-classification
- generated_from_trainer
datasets:
- beavertails
metrics:
- accuracy
model-index:
- name: QA-DeBERTa-v3-large-diff-binary-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: saiteki-kai/Beavertails-it
type: beavertails
metrics:
- name: Accuracy
type: accuracy
value: 0.8608643577203313
---
<!-- 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-diff-binary-2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the saiteki-kai/Beavertails-it dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3409
- Accuracy: 0.8609
- Unsafe Precision: 0.8682
- Unsafe Recall: 0.8842
- Unsafe F1: 0.8761
- Unsafe Fpr: 0.1684
- Unsafe Aucpr: 0.9538
- Safe Precision: 0.8512
- Safe Recall: 0.8316
- Safe F1: 0.8413
- Safe Fpr: 0.1158
- Safe Aucpr: 0.9184
## 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: 6e-06
- train_batch_size: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:----------:|:------------:|:--------------:|:-----------:|:-------:|:--------:|:----------:|
| 0.2998 | 0.2501 | 2114 | 0.3677 | 0.8446 | 0.9027 | 0.8078 | 0.8526 | 0.1093 | 0.9436 | 0.7870 | 0.8907 | 0.8356 | 0.1922 | 0.8961 |
| 0.3262 | 0.5001 | 4228 | 0.3278 | 0.8561 | 0.8786 | 0.8602 | 0.8693 | 0.1491 | 0.9495 | 0.8291 | 0.8509 | 0.8399 | 0.1398 | 0.9087 |
| 0.3019 | 0.7502 | 6342 | 0.3236 | 0.8588 | 0.8972 | 0.8429 | 0.8692 | 0.1211 | 0.9527 | 0.8168 | 0.8789 | 0.8467 | 0.1571 | 0.9155 |
| 0.3479 | 1.0002 | 8456 | 0.3215 | 0.8599 | 0.8690 | 0.8811 | 0.8750 | 0.1666 | 0.9531 | 0.8482 | 0.8334 | 0.8407 | 0.1189 | 0.9175 |
| 0.302 | 1.2503 | 10570 | 0.3221 | 0.8611 | 0.8839 | 0.8639 | 0.8738 | 0.1423 | 0.9536 | 0.8340 | 0.8577 | 0.8457 | 0.1361 | 0.9176 |
| 0.2663 | 1.5004 | 12684 | 0.3409 | 0.8609 | 0.8682 | 0.8842 | 0.8761 | 0.1684 | 0.9538 | 0.8512 | 0.8316 | 0.8413 | 0.1158 | 0.9184 |
| 0.2841 | 1.7504 | 14798 | 0.3223 | 0.8622 | 0.8772 | 0.8748 | 0.8760 | 0.1537 | 0.9551 | 0.8435 | 0.8463 | 0.8449 | 0.1252 | 0.9202 |
| 0.3074 | 2.0005 | 16912 | 0.3244 | 0.8632 | 0.8995 | 0.8490 | 0.8735 | 0.1190 | 0.9553 | 0.8230 | 0.8810 | 0.8510 | 0.1510 | 0.9182 |
| 0.3052 | 2.2505 | 19026 | 0.3200 | 0.8618 | 0.8833 | 0.8660 | 0.8746 | 0.1435 | 0.9546 | 0.8359 | 0.8565 | 0.8461 | 0.1340 | 0.9221 |
| 0.268 | 2.5006 | 21140 | 0.3192 | 0.8627 | 0.8876 | 0.8625 | 0.8748 | 0.1370 | 0.9550 | 0.8334 | 0.8630 | 0.8479 | 0.1375 | 0.9220 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.7.1+cu118
- Datasets 4.4.1
- Tokenizers 0.22.1
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