distilbert_add_GLUE_Experiment_qnli_192
This model is a fine-tuned version of distilbert-base-uncased on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6649
- Accuracy: 0.5949
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 |
|---|---|---|---|---|
| 0.6936 | 1.0 | 410 | 0.6930 | 0.5054 |
| 0.6793 | 2.0 | 820 | 0.6684 | 0.5823 |
| 0.6511 | 3.0 | 1230 | 0.6650 | 0.5938 |
| 0.6385 | 4.0 | 1640 | 0.6649 | 0.5949 |
| 0.6306 | 5.0 | 2050 | 0.6668 | 0.5923 |
| 0.6215 | 6.0 | 2460 | 0.6783 | 0.5931 |
| 0.6137 | 7.0 | 2870 | 0.6969 | 0.5852 |
| 0.6046 | 8.0 | 3280 | 0.6888 | 0.5881 |
| 0.5964 | 9.0 | 3690 | 0.6977 | 0.5799 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2