distilbert_add_GLUE_Experiment_logit_kd_wnli
This model is a fine-tuned version of distilbert-base-uncased on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.3434
- Accuracy: 0.5634
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 |
|---|---|---|---|---|
| 0.5945 | 1.0 | 3 | 0.3447 | 0.5634 |
| 0.4026 | 2.0 | 6 | 0.3814 | 0.4366 |
| 0.3533 | 3.0 | 9 | 0.3511 | 0.5634 |
| 0.3557 | 4.0 | 12 | 0.3593 | 0.4366 |
| 0.3558 | 5.0 | 15 | 0.3435 | 0.5634 |
| 0.3496 | 6.0 | 18 | 0.3434 | 0.5634 |
| 0.3497 | 7.0 | 21 | 0.3499 | 0.4366 |
| 0.3511 | 8.0 | 24 | 0.3439 | 0.5634 |
| 0.3551 | 9.0 | 27 | 0.3434 | 0.5634 |
| 0.3498 | 10.0 | 30 | 0.3502 | 0.4366 |
| 0.3496 | 11.0 | 33 | 0.3442 | 0.5634 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2