| --- |
| library_name: transformers |
| 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-MeanPooling-binary |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: saiteki-kai/Beavertails-it |
| type: beavertails |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.864490800811791 |
| --- |
| |
| <!-- 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-MeanPooling-binary |
|
|
| 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: 0.3246 |
| - Accuracy: 0.8645 |
| - Unsafe Precision: 0.8883 |
| - Unsafe Recall: 0.8653 |
| - Unsafe F1: 0.8766 |
| - Unsafe Fpr: 0.1366 |
| - Unsafe Aucpr: 0.9567 |
| - Safe Precision: 0.8363 |
| - Safe Recall: 0.8634 |
| - Safe F1: 0.8497 |
| - Safe Fpr: 0.1347 |
| - Safe Aucpr: 0.9241 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - 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_ratio: 0.03 |
| - num_epochs: 3 |
| |
| ### 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 | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:----------:|:------------:|:--------------:|:-----------:|:-------:|:--------:|:----------:| |
| | 0.3568 | 0.3001 | 5073 | 0.3409 | 0.8500 | 0.8498 | 0.8871 | 0.8681 | 0.1966 | 0.9480 | 0.8501 | 0.8034 | 0.8261 | 0.1129 | 0.9069 | |
| | 0.3161 | 0.6001 | 10146 | 0.3243 | 0.8592 | 0.8766 | 0.8693 | 0.8729 | 0.1536 | 0.9519 | 0.8377 | 0.8464 | 0.8420 | 0.1307 | 0.9135 | |
| | 0.304 | 0.9002 | 15219 | 0.3204 | 0.8609 | 0.8710 | 0.8805 | 0.8757 | 0.1636 | 0.9537 | 0.8480 | 0.8364 | 0.8422 | 0.1195 | 0.9169 | |
| | 0.3128 | 1.2002 | 20292 | 0.3311 | 0.8627 | 0.8932 | 0.8556 | 0.8740 | 0.1283 | 0.9532 | 0.8279 | 0.8717 | 0.8493 | 0.1444 | 0.9191 | |
| | 0.3198 | 1.5003 | 25365 | 0.3146 | 0.8624 | 0.8815 | 0.8695 | 0.8755 | 0.1466 | 0.9555 | 0.8391 | 0.8534 | 0.8462 | 0.1305 | 0.9225 | |
| | 0.2852 | 1.8003 | 30438 | 0.3174 | 0.8647 | 0.8937 | 0.8591 | 0.8761 | 0.1282 | 0.9568 | 0.8314 | 0.8718 | 0.8511 | 0.1409 | 0.9240 | |
| | 0.2694 | 2.1004 | 35511 | 0.3260 | 0.8642 | 0.8895 | 0.8633 | 0.8762 | 0.1346 | 0.9566 | 0.8346 | 0.8654 | 0.8497 | 0.1367 | 0.9231 | |
| | 0.3003 | 2.4004 | 40584 | 0.3265 | 0.8640 | 0.8850 | 0.8683 | 0.8766 | 0.1415 | 0.9568 | 0.8386 | 0.8585 | 0.8484 | 0.1317 | 0.9239 | |
| | 0.2723 | 2.7005 | 45657 | 0.3246 | 0.8644 | 0.8882 | 0.8653 | 0.8766 | 0.1366 | 0.9567 | 0.8363 | 0.8634 | 0.8496 | 0.1347 | 0.9241 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.1 |
| - Pytorch 2.9.1+cu128 |
| - Datasets 4.4.1 |
| - Tokenizers 0.22.1 |
| |
