distilbert_sa_GLUE_Experiment_data_aug_cola_256
This model is a fine-tuned version of distilbert-base-uncased on the GLUE COLA dataset. It achieves the following results on the evaluation set:
- Loss: 0.6572
- Matthews Correlation: 0.0849
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 | Matthews Correlation |
|---|---|---|---|---|
| 0.5384 | 1.0 | 835 | 0.6572 | 0.0849 |
| 0.409 | 2.0 | 1670 | 0.7809 | 0.1141 |
| 0.3334 | 3.0 | 2505 | 0.9039 | 0.1089 |
| 0.2753 | 4.0 | 3340 | 1.0123 | 0.1388 |
| 0.23 | 5.0 | 4175 | 1.1064 | 0.1382 |
| 0.1927 | 6.0 | 5010 | 1.2698 | 0.1158 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2