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distilbert_sa_GLUE_Experiment_logit_kd_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: 0.7047
- Accuracy: 0.6401
- F1: 0.0484
- Combined Score: 0.3442
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 |
|---|---|---|---|---|---|---|
| 0.824 | 1.0 | 1422 | 0.7761 | 0.6318 | 0.0 | 0.3159 |
| 0.7581 | 2.0 | 2844 | 0.7544 | 0.6318 | 0.0 | 0.3159 |
| 0.7333 | 3.0 | 4266 | 0.7480 | 0.6318 | 0.0 | 0.3159 |
| 0.713 | 4.0 | 5688 | 0.7358 | 0.6318 | 0.0 | 0.3159 |
| 0.695 | 5.0 | 7110 | 0.7279 | 0.6365 | 0.0280 | 0.3323 |
| 0.6783 | 6.0 | 8532 | 0.7193 | 0.6370 | 0.0313 | 0.3341 |
| 0.6642 | 7.0 | 9954 | 0.7148 | 0.6332 | 0.0083 | 0.3208 |
| 0.6509 | 8.0 | 11376 | 0.7138 | 0.6409 | 0.0536 | 0.3472 |
| 0.639 | 9.0 | 12798 | 0.7084 | 0.6385 | 0.0398 | 0.3391 |
| 0.6287 | 10.0 | 14220 | 0.7117 | 0.6418 | 0.0591 | 0.3504 |
| 0.6195 | 11.0 | 15642 | 0.7064 | 0.6379 | 0.0363 | 0.3371 |
| 0.6101 | 12.0 | 17064 | 0.7079 | 0.6444 | 0.0725 | 0.3584 |
| 0.6023 | 13.0 | 18486 | 0.7059 | 0.6495 | 0.1028 | 0.3762 |
| 0.5948 | 14.0 | 19908 | 0.7103 | 0.6418 | 0.0577 | 0.3497 |
| 0.589 | 15.0 | 21330 | 0.7077 | 0.6415 | 0.0567 | 0.3491 |
| 0.5827 | 16.0 | 22752 | 0.7047 | 0.6401 | 0.0484 | 0.3442 |
| 0.5769 | 17.0 | 24174 | 0.7051 | 0.6409 | 0.0525 | 0.3467 |
| 0.572 | 18.0 | 25596 | 0.7090 | 0.6492 | 0.1017 | 0.3754 |
| 0.5673 | 19.0 | 27018 | 0.7078 | 0.6398 | 0.0467 | 0.3433 |
| 0.563 | 20.0 | 28440 | 0.7065 | 0.6427 | 0.0630 | 0.3529 |
| 0.5591 | 21.0 | 29862 | 0.7084 | 0.6464 | 0.0839 | 0.3651 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
