distilbert_sa_GLUE_Experiment_data_aug_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: 0.9477
- Accuracy: 0.5655
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 |
|---|---|---|---|---|
| 0.9142 | 1.0 | 31440 | 0.9328 | 0.5686 |
| 0.8099 | 2.0 | 62880 | 0.9523 | 0.5752 |
| 0.7371 | 3.0 | 94320 | 1.0072 | 0.5737 |
| 0.6756 | 4.0 | 125760 | 1.0606 | 0.5750 |
| 0.6229 | 5.0 | 157200 | 1.1116 | 0.5739 |
| 0.5784 | 6.0 | 188640 | 1.1396 | 0.5795 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2