distilbert_add_GLUE_Experiment_mnli_96
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: 1.0256
- Accuracy: 0.5004
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 |
|---|---|---|---|---|
| 1.0987 | 1.0 | 1534 | 1.0980 | 0.3545 |
| 1.0979 | 2.0 | 3068 | 1.0942 | 0.3580 |
| 1.0897 | 3.0 | 4602 | 1.0896 | 0.3706 |
| 1.0817 | 4.0 | 6136 | 1.0769 | 0.3991 |
| 1.072 | 5.0 | 7670 | 1.0680 | 0.4146 |
| 1.0603 | 6.0 | 9204 | 1.0700 | 0.4174 |
| 1.0515 | 7.0 | 10738 | 1.0655 | 0.4179 |
| 1.0441 | 8.0 | 12272 | 1.0546 | 0.4335 |
| 1.038 | 9.0 | 13806 | 1.0751 | 0.4059 |
| 1.0344 | 10.0 | 15340 | 1.0554 | 0.4363 |
| 1.0275 | 11.0 | 16874 | 1.0736 | 0.4207 |
| 1.0225 | 12.0 | 18408 | 1.0662 | 0.4295 |
| 1.0169 | 13.0 | 19942 | 1.0544 | 0.4421 |
| 1.0111 | 14.0 | 21476 | 1.0635 | 0.4411 |
| 1.0043 | 15.0 | 23010 | 1.0505 | 0.4567 |
| 0.9986 | 16.0 | 24544 | 1.0402 | 0.4643 |
| 0.9925 | 17.0 | 26078 | 1.0531 | 0.4545 |
| 0.9861 | 18.0 | 27612 | 1.0431 | 0.4675 |
| 0.9781 | 19.0 | 29146 | 1.0361 | 0.4801 |
| 0.9673 | 20.0 | 30680 | 1.0301 | 0.4879 |
| 0.9552 | 21.0 | 32214 | 1.0327 | 0.4908 |
| 0.9467 | 22.0 | 33748 | 1.0248 | 0.5013 |
| 0.9396 | 23.0 | 35282 | 1.0297 | 0.4977 |
| 0.9328 | 24.0 | 36816 | 1.0237 | 0.5025 |
| 0.9277 | 25.0 | 38350 | 1.0384 | 0.5010 |
| 0.9228 | 26.0 | 39884 | 1.0374 | 0.5037 |
| 0.918 | 27.0 | 41418 | 1.0242 | 0.5006 |
| 0.9128 | 28.0 | 42952 | 1.0248 | 0.5060 |
| 0.9087 | 29.0 | 44486 | 1.0283 | 0.5027 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2