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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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tags: |
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- single_label_classification |
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- question-answering |
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- text-classification |
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- generated_from_trainer |
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datasets: |
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- beavertails |
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metrics: |
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- accuracy |
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model-index: |
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- name: QA-DeBERTa-v3-large-diff-binary-2 |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: saiteki-kai/Beavertails-it |
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type: beavertails |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8608643577203313 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# QA-DeBERTa-v3-large-diff-binary-2 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3409 |
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- Accuracy: 0.8609 |
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- Unsafe Precision: 0.8682 |
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- Unsafe Recall: 0.8842 |
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- Unsafe F1: 0.8761 |
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- Unsafe Fpr: 0.1684 |
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- Unsafe Aucpr: 0.9538 |
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- Safe Precision: 0.8512 |
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- Safe Recall: 0.8316 |
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- Safe F1: 0.8413 |
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- Safe Fpr: 0.1158 |
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- Safe Aucpr: 0.9184 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-06 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 10 |
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### Training results |
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| 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 | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:----------:|:------------:|:--------------:|:-----------:|:-------:|:--------:|:----------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.7.1+cu118 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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