distilbert_sa_GLUE_Experiment_data_aug_wnli_96
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: 0.6935
- 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 |
|---|---|---|---|---|
| 0.693 | 1.0 | 218 | 0.6935 | 0.5634 |
| 0.6226 | 2.0 | 436 | 1.4150 | 0.1549 |
| 0.5091 | 3.0 | 654 | 1.7966 | 0.1268 |
| 0.4594 | 4.0 | 872 | 2.1812 | 0.1127 |
| 0.4125 | 5.0 | 1090 | 2.6036 | 0.0845 |
| 0.3697 | 6.0 | 1308 | 3.0124 | 0.0704 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2