distilbert_add_GLUE_Experiment_logit_kd_qnli_256
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: 0.3989
- Accuracy: 0.5874
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 |
|---|---|---|---|---|
| 0.4156 | 1.0 | 410 | 0.4111 | 0.5054 |
| 0.4078 | 2.0 | 820 | 0.4018 | 0.5799 |
| 0.3962 | 3.0 | 1230 | 0.3989 | 0.5874 |
| 0.3899 | 4.0 | 1640 | 0.4018 | 0.5867 |
| 0.3851 | 5.0 | 2050 | 0.4032 | 0.5799 |
| 0.3802 | 6.0 | 2460 | 0.4118 | 0.5728 |
| 0.3762 | 7.0 | 2870 | 0.4093 | 0.5718 |
| 0.3717 | 8.0 | 3280 | 0.4100 | 0.5737 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2