DeBERTa-v3-small fine-tuned on QNLI
This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.2143
- Accuracy: 0.9151
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2823 | 1.0 | 6547 | 0.2143 | 0.9151 |
| 0.1996 | 2.0 | 13094 | 0.2760 | 0.9103 |
| 0.1327 | 3.0 | 19641 | 0.3293 | 0.9169 |
| 0.0811 | 4.0 | 26188 | 0.4278 | 0.9193 |
| 0.05 | 5.0 | 32735 | 0.5110 | 0.9176 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
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Dataset used to train mrm8488/deberta-v3-small-finetuned-qnli
Evaluation results
- Accuracy on GLUE QNLIself-reported0.915
- Accuracy on gluevalidation set verified0.915
- Precision on gluevalidation set verified0.920
- Recall on gluevalidation set verified0.911
- AUC on gluevalidation set verified0.972
- F1 on gluevalidation set verified0.915
- loss on gluevalidation set verified0.214