distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp_192
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: 0.7029
- Accuracy: 0.6540
- F1: 0.1254
- Combined Score: 0.3897
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 |
|---|---|---|---|---|---|---|
| 0.8495 | 1.0 | 29671 | 0.7150 | 0.6333 | 0.0086 | 0.3210 |
| 0.7654 | 2.0 | 59342 | 0.7273 | 0.6339 | 0.0121 | 0.3230 |
| 0.7305 | 3.0 | 89013 | 0.7241 | 0.6400 | 0.0479 | 0.3440 |
| 0.7108 | 4.0 | 118684 | 0.7147 | 0.6381 | 0.0380 | 0.3381 |
| 0.698 | 5.0 | 148355 | 0.7192 | 0.6414 | 0.0564 | 0.3489 |
| 0.6891 | 6.0 | 178026 | 0.7239 | 0.6357 | 0.0232 | 0.3295 |
| 0.6823 | 7.0 | 207697 | 0.7141 | 0.6442 | 0.0723 | 0.3583 |
| 0.6771 | 8.0 | 237368 | 0.7112 | 0.6491 | 0.1004 | 0.3748 |
| 0.6729 | 9.0 | 267039 | 0.7156 | 0.6494 | 0.1022 | 0.3758 |
| 0.6694 | 10.0 | 296710 | 0.7185 | 0.6502 | 0.1053 | 0.3777 |
| 0.6664 | 11.0 | 326381 | 0.7129 | 0.6508 | 0.1085 | 0.3796 |
| 0.6639 | 12.0 | 356052 | 0.7112 | 0.6508 | 0.1080 | 0.3794 |
| 0.6617 | 13.0 | 385723 | 0.7105 | 0.6542 | 0.1260 | 0.3901 |
| 0.6597 | 14.0 | 415394 | 0.7029 | 0.6540 | 0.1254 | 0.3897 |
| 0.658 | 15.0 | 445065 | 0.7094 | 0.6486 | 0.0964 | 0.3725 |
| 0.6564 | 16.0 | 474736 | 0.7072 | 0.6510 | 0.1084 | 0.3797 |
| 0.655 | 17.0 | 504407 | 0.7049 | 0.6557 | 0.1333 | 0.3945 |
| 0.6537 | 18.0 | 534078 | 0.7051 | 0.6542 | 0.1269 | 0.3905 |
| 0.6526 | 19.0 | 563749 | 0.7096 | 0.6601 | 0.1573 | 0.4087 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2