| --- |
| 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-IGPooling-binary |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: saiteki-kai/Beavertails-it |
| type: beavertails |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.8652227434541039 |
| --- |
| |
| <!-- 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-IGPooling-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: 0.4138 |
| - Accuracy: 0.8652 |
| - Unsafe Precision: 0.8925 |
| - Unsafe Recall: 0.8616 |
| - Unsafe F1: 0.8768 |
| - Unsafe Fpr: 0.1302 |
| - Unsafe Aucpr: 0.9567 |
| - Safe Precision: 0.8336 |
| - Safe Recall: 0.8698 |
| - Safe F1: 0.8513 |
| - Safe Fpr: 0.1384 |
| - Safe Aucpr: 0.9230 |
|
|
| ## 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 | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:----------:|:------------:|:--------------:|:-----------:|:-------:|:--------:|:----------:| |
| | 0.4537 | 0.3001 | 5073 | 0.4406 | 0.8504 | 0.8537 | 0.8822 | 0.8678 | 0.1896 | 0.9479 | 0.8458 | 0.8104 | 0.8277 | 0.1178 | 0.9067 | |
| | 0.4184 | 0.6001 | 10146 | 0.4195 | 0.8592 | 0.8815 | 0.8631 | 0.8722 | 0.1456 | 0.9519 | 0.8326 | 0.8544 | 0.8434 | 0.1369 | 0.9133 | |
| | 0.4022 | 0.9002 | 15219 | 0.4160 | 0.8613 | 0.8783 | 0.8717 | 0.8749 | 0.1516 | 0.9536 | 0.8405 | 0.8484 | 0.8444 | 0.1283 | 0.9172 | |
| | 0.3991 | 1.2002 | 20292 | 0.4309 | 0.8618 | 0.8945 | 0.8521 | 0.8728 | 0.1261 | 0.9534 | 0.8248 | 0.8739 | 0.8487 | 0.1479 | 0.9193 | |
| | 0.4094 | 1.5003 | 25365 | 0.4102 | 0.8634 | 0.8847 | 0.8675 | 0.8760 | 0.1418 | 0.9554 | 0.8377 | 0.8582 | 0.8478 | 0.1325 | 0.9220 | |
| | 0.3822 | 1.8003 | 30438 | 0.4139 | 0.8652 | 0.8924 | 0.8616 | 0.8767 | 0.1303 | 0.9567 | 0.8336 | 0.8697 | 0.8512 | 0.1384 | 0.9230 | |
| | 0.3505 | 2.1004 | 35511 | 0.4229 | 0.8639 | 0.8871 | 0.8656 | 0.8762 | 0.1382 | 0.9565 | 0.8363 | 0.8618 | 0.8489 | 0.1344 | 0.9226 | |
| | 0.4044 | 2.4004 | 40584 | 0.4270 | 0.8634 | 0.8824 | 0.8705 | 0.8764 | 0.1455 | 0.9566 | 0.8403 | 0.8545 | 0.8473 | 0.1295 | 0.9236 | |
| | 0.3484 | 2.7005 | 45657 | 0.4222 | 0.8638 | 0.8860 | 0.8668 | 0.8763 | 0.1399 | 0.9567 | 0.8373 | 0.8601 | 0.8485 | 0.1332 | 0.9238 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.1 |
| - Pytorch 2.9.1+cu128 |
| - Datasets 4.4.1 |
| - Tokenizers 0.22.1 |
| |
