distilbert_add_GLUE_Experiment_qnli
This model is a fine-tuned version of distilbert-base-uncased on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6648
- Accuracy: 0.6066
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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6886 | 1.0 | 410 | 0.6648 | 0.6066 |
| 0.6569 | 2.0 | 820 | 0.6677 | 0.5999 |
| 0.6419 | 3.0 | 1230 | 0.6672 | 0.5914 |
| 0.6293 | 4.0 | 1640 | 0.6677 | 0.5977 |
| 0.6118 | 5.0 | 2050 | 0.6691 | 0.6002 |
| 0.5857 | 6.0 | 2460 | 0.6854 | 0.6077 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
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Dataset used to train gokuls/distilbert_add_GLUE_Experiment_qnli
Evaluation results
- Accuracy on GLUE QNLIvalidation set self-reported0.607