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distilbert_add_GLUE_Experiment_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: 0.9590
- Accuracy: 0.5259
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 |
|---|---|---|---|---|
| 1.096 | 1.0 | 1534 | 1.0536 | 0.4466 |
| 1.0362 | 2.0 | 3068 | 1.0527 | 0.4579 |
| 1.0213 | 3.0 | 4602 | 1.0341 | 0.4606 |
| 1.0085 | 4.0 | 6136 | 1.0170 | 0.4810 |
| 0.9971 | 5.0 | 7670 | 1.0013 | 0.4868 |
| 0.9877 | 6.0 | 9204 | 0.9913 | 0.4950 |
| 0.9805 | 7.0 | 10738 | 0.9872 | 0.4986 |
| 0.9726 | 8.0 | 12272 | 0.9822 | 0.5019 |
| 0.9658 | 9.0 | 13806 | 0.9812 | 0.5115 |
| 0.9566 | 10.0 | 15340 | 0.9761 | 0.5179 |
| 0.9439 | 11.0 | 16874 | 0.9650 | 0.5261 |
| 0.9336 | 12.0 | 18408 | 0.9616 | 0.5283 |
| 0.9232 | 13.0 | 19942 | 0.9620 | 0.5306 |
| 0.9145 | 14.0 | 21476 | 0.9663 | 0.5329 |
| 0.9056 | 15.0 | 23010 | 0.9491 | 0.5408 |
| 0.8986 | 16.0 | 24544 | 0.9567 | 0.5389 |
| 0.8903 | 17.0 | 26078 | 0.9488 | 0.5382 |
| 0.8835 | 18.0 | 27612 | 0.9739 | 0.5202 |
| 0.8769 | 19.0 | 29146 | 0.9761 | 0.5382 |
| 0.8701 | 20.0 | 30680 | 0.9504 | 0.5439 |
| 0.8633 | 21.0 | 32214 | 1.0074 | 0.5265 |
| 0.8576 | 22.0 | 33748 | 0.9874 | 0.5392 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
