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mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_stsb
This model is a fine-tuned version of gokuls/mobilebert_sa_pre-training-complete on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 0.2919
- Pearson: 0.8665
- Spearmanr: 0.8642
- Combined Score: 0.8654
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: 128
- eval_batch_size: 128
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 1.1501 | 1.0 | 45 | 0.4726 | 0.7774 | 0.7922 | 0.7848 |
| 0.364 | 2.0 | 90 | 0.3480 | 0.8457 | 0.8455 | 0.8456 |
| 0.259 | 3.0 | 135 | 0.3156 | 0.8582 | 0.8590 | 0.8586 |
| 0.2054 | 4.0 | 180 | 0.4231 | 0.8551 | 0.8549 | 0.8550 |
| 0.1629 | 5.0 | 225 | 0.3245 | 0.8668 | 0.8654 | 0.8661 |
| 0.1263 | 6.0 | 270 | 0.3192 | 0.8649 | 0.8625 | 0.8637 |
| 0.1021 | 7.0 | 315 | 0.3337 | 0.8655 | 0.8629 | 0.8642 |
| 0.0841 | 8.0 | 360 | 0.3061 | 0.8601 | 0.8577 | 0.8589 |
| 0.0713 | 9.0 | 405 | 0.3600 | 0.8576 | 0.8555 | 0.8566 |
| 0.0587 | 10.0 | 450 | 0.3135 | 0.8620 | 0.8600 | 0.8610 |
| 0.0488 | 11.0 | 495 | 0.3006 | 0.8641 | 0.8620 | 0.8631 |
| 0.0441 | 12.0 | 540 | 0.3308 | 0.8645 | 0.8621 | 0.8633 |
| 0.0385 | 13.0 | 585 | 0.3468 | 0.8620 | 0.8601 | 0.8610 |
| 0.0346 | 14.0 | 630 | 0.3175 | 0.8658 | 0.8634 | 0.8646 |
| 0.0298 | 15.0 | 675 | 0.2919 | 0.8665 | 0.8642 | 0.8654 |
| 0.0299 | 16.0 | 720 | 0.3103 | 0.8649 | 0.8628 | 0.8639 |
| 0.0263 | 17.0 | 765 | 0.3325 | 0.8620 | 0.8599 | 0.8609 |
| 0.0237 | 18.0 | 810 | 0.3092 | 0.8636 | 0.8611 | 0.8623 |
| 0.0213 | 19.0 | 855 | 0.3169 | 0.8653 | 0.8631 | 0.8642 |
| 0.0196 | 20.0 | 900 | 0.2985 | 0.8647 | 0.8624 | 0.8636 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
