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distilbert_add_GLUE_Experiment_logit_kd_qqp_256
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: 0.6586
- Accuracy: 0.6554
- F1: 0.1310
- Combined Score: 0.3932
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 |
---|---|---|---|---|---|---|
0.8355 | 1.0 | 1422 | 0.8004 | 0.6318 | 0.0 | 0.3159 |
0.7677 | 2.0 | 2844 | 0.7488 | 0.6318 | 0.0 | 0.3159 |
0.7048 | 3.0 | 4266 | 0.6935 | 0.6318 | 0.0 | 0.3159 |
0.6449 | 4.0 | 5688 | 0.6875 | 0.6337 | 0.0106 | 0.3221 |
0.6082 | 5.0 | 7110 | 0.6688 | 0.6354 | 0.0205 | 0.3279 |
0.5829 | 6.0 | 8532 | 0.6854 | 0.6386 | 0.0393 | 0.3389 |
0.5637 | 7.0 | 9954 | 0.6707 | 0.6522 | 0.1155 | 0.3839 |
0.5502 | 8.0 | 11376 | 0.6752 | 0.6522 | 0.1145 | 0.3833 |
0.5389 | 9.0 | 12798 | 0.6677 | 0.6561 | 0.1348 | 0.3954 |
0.5304 | 10.0 | 14220 | 0.6693 | 0.6622 | 0.1659 | 0.4140 |
0.5234 | 11.0 | 15642 | 0.6728 | 0.6511 | 0.1082 | 0.3797 |
0.5175 | 12.0 | 17064 | 0.6812 | 0.6554 | 0.1303 | 0.3928 |
0.5127 | 13.0 | 18486 | 0.6644 | 0.6540 | 0.1235 | 0.3888 |
0.5085 | 14.0 | 19908 | 0.6605 | 0.6622 | 0.1677 | 0.4149 |
0.505 | 15.0 | 21330 | 0.6647 | 0.6570 | 0.1391 | 0.3980 |
0.502 | 16.0 | 22752 | 0.6667 | 0.6528 | 0.1170 | 0.3849 |
0.499 | 17.0 | 24174 | 0.6586 | 0.6554 | 0.1310 | 0.3932 |
0.497 | 18.0 | 25596 | 0.6589 | 0.6597 | 0.1535 | 0.4066 |
0.4947 | 19.0 | 27018 | 0.6715 | 0.6599 | 0.1535 | 0.4067 |
0.4928 | 20.0 | 28440 | 0.6631 | 0.6535 | 0.1202 | 0.3868 |
0.4907 | 21.0 | 29862 | 0.6690 | 0.6651 | 0.1796 | 0.4224 |
0.4891 | 22.0 | 31284 | 0.6603 | 0.6652 | 0.1830 | 0.4241 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2