distilbert_sa_GLUE_Experiment_data_aug_rte_256
This model is a fine-tuned version of distilbert-base-uncased on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 1.9555
- Accuracy: 0.5054
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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.2767 | 1.0 | 568 | 0.5054 | 1.9555 |
| 0.05 | 2.0 | 1136 | 2.3512 | 0.4874 |
| 0.0262 | 3.0 | 1704 | 2.6338 | 0.4838 |
| 0.0175 | 4.0 | 2272 | 3.0118 | 0.5090 |
| 0.0116 | 5.0 | 2840 | 3.1543 | 0.5126 |
| 0.0087 | 6.0 | 3408 | 3.4710 | 0.5054 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2