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distilbert_sa_GLUE_Experiment_logit_kd_mnli_192
This model is a fine-tuned version of distilbert-base-uncased on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.5326
- Accuracy: 0.5774
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 |
|---|---|---|---|---|
| 0.5885 | 1.0 | 1534 | 0.5621 | 0.5096 |
| 0.5572 | 2.0 | 3068 | 0.5481 | 0.5303 |
| 0.5456 | 3.0 | 4602 | 0.5473 | 0.5347 |
| 0.5373 | 4.0 | 6136 | 0.5404 | 0.5533 |
| 0.5299 | 5.0 | 7670 | 0.5371 | 0.5629 |
| 0.5235 | 6.0 | 9204 | 0.5361 | 0.5671 |
| 0.5172 | 7.0 | 10738 | 0.5360 | 0.5645 |
| 0.5114 | 8.0 | 12272 | 0.5391 | 0.5664 |
| 0.5058 | 9.0 | 13806 | 0.5332 | 0.5839 |
| 0.5004 | 10.0 | 15340 | 0.5294 | 0.5867 |
| 0.4951 | 11.0 | 16874 | 0.5284 | 0.5905 |
| 0.4901 | 12.0 | 18408 | 0.5309 | 0.5892 |
| 0.4853 | 13.0 | 19942 | 0.5334 | 0.5967 |
| 0.4804 | 14.0 | 21476 | 0.5344 | 0.5954 |
| 0.4754 | 15.0 | 23010 | 0.5297 | 0.5987 |
| 0.4707 | 16.0 | 24544 | 0.5348 | 0.5989 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
