distilbert_sa_GLUE_Experiment_logit_kd_qqp_96
This model is a fine-tuned version of distilbert-base-uncased on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.7423
- Accuracy: 0.6329
- F1: 0.0062
- Combined Score: 0.3195
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 | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.8963 | 1.0 | 1422 | 0.7832 | 0.6318 | 0.0 | 0.3159 |
| 0.7734 | 2.0 | 2844 | 0.7741 | 0.6318 | 0.0 | 0.3159 |
| 0.7598 | 3.0 | 4266 | 0.7727 | 0.6318 | 0.0 | 0.3159 |
| 0.7474 | 4.0 | 5688 | 0.7675 | 0.6318 | 0.0 | 0.3159 |
| 0.7366 | 5.0 | 7110 | 0.7626 | 0.6318 | 0.0 | 0.3159 |
| 0.7272 | 6.0 | 8532 | 0.7568 | 0.6318 | 0.0 | 0.3159 |
| 0.7177 | 7.0 | 9954 | 0.7539 | 0.6318 | 0.0 | 0.3159 |
| 0.7084 | 8.0 | 11376 | 0.7500 | 0.6318 | 0.0 | 0.3159 |
| 0.6998 | 9.0 | 12798 | 0.7543 | 0.6318 | 0.0 | 0.3159 |
| 0.692 | 10.0 | 14220 | 0.7469 | 0.6318 | 0.0 | 0.3159 |
| 0.6846 | 11.0 | 15642 | 0.7481 | 0.6318 | 0.0 | 0.3159 |
| 0.6774 | 12.0 | 17064 | 0.7486 | 0.6318 | 0.0 | 0.3159 |
| 0.6705 | 13.0 | 18486 | 0.7440 | 0.6318 | 0.0 | 0.3159 |
| 0.6648 | 14.0 | 19908 | 0.7464 | 0.6318 | 0.0 | 0.3159 |
| 0.659 | 15.0 | 21330 | 0.7430 | 0.6318 | 0.0 | 0.3159 |
| 0.6531 | 16.0 | 22752 | 0.7423 | 0.6329 | 0.0062 | 0.3195 |
| 0.6479 | 17.0 | 24174 | 0.7452 | 0.6321 | 0.0016 | 0.3169 |
| 0.643 | 18.0 | 25596 | 0.7443 | 0.6354 | 0.0214 | 0.3284 |
| 0.6387 | 19.0 | 27018 | 0.7431 | 0.6335 | 0.0092 | 0.3213 |
| 0.6343 | 20.0 | 28440 | 0.7436 | 0.6370 | 0.0318 | 0.3344 |
| 0.6297 | 21.0 | 29862 | 0.7444 | 0.6362 | 0.0266 | 0.3314 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2