distilbert_sa_GLUE_Experiment_logit_kd_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: 0.3931
- Accuracy: 0.5870
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 |
|---|---|---|---|---|
| 0.4083 | 1.0 | 410 | 0.3946 | 0.5735 |
| 0.3936 | 2.0 | 820 | 0.3931 | 0.5870 |
| 0.3843 | 3.0 | 1230 | 0.3935 | 0.5863 |
| 0.3766 | 4.0 | 1640 | 0.3980 | 0.5858 |
| 0.3699 | 5.0 | 2050 | 0.3996 | 0.5781 |
| 0.3636 | 6.0 | 2460 | 0.4112 | 0.5795 |
| 0.3572 | 7.0 | 2870 | 0.4269 | 0.5667 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2