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distilbert_add_GLUE_Experiment_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: 0.4726
- Accuracy: 0.7906
- F1: 0.7104
- Combined Score: 0.7505
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 |
|---|---|---|---|---|---|---|
| 0.5993 | 1.0 | 1422 | 0.5459 | 0.7243 | 0.6353 | 0.6798 |
| 0.5167 | 2.0 | 2844 | 0.5176 | 0.7471 | 0.6481 | 0.6976 |
| 0.4956 | 3.0 | 4266 | 0.5036 | 0.7588 | 0.6463 | 0.7025 |
| 0.4849 | 4.0 | 5688 | 0.5056 | 0.7546 | 0.6610 | 0.7078 |
| 0.4762 | 5.0 | 7110 | 0.5127 | 0.7530 | 0.6705 | 0.7118 |
| 0.4689 | 6.0 | 8532 | 0.5218 | 0.7476 | 0.6754 | 0.7115 |
| 0.4622 | 7.0 | 9954 | 0.4935 | 0.7661 | 0.6571 | 0.7116 |
| 0.4554 | 8.0 | 11376 | 0.5039 | 0.7605 | 0.6537 | 0.7071 |
| 0.4483 | 9.0 | 12798 | 0.5009 | 0.7625 | 0.6732 | 0.7178 |
| 0.4393 | 10.0 | 14220 | 0.4991 | 0.7594 | 0.6857 | 0.7226 |
| 0.4293 | 11.0 | 15642 | 0.4857 | 0.7761 | 0.6548 | 0.7155 |
| 0.4162 | 12.0 | 17064 | 0.4897 | 0.7735 | 0.6935 | 0.7335 |
| 0.4021 | 13.0 | 18486 | 0.4758 | 0.7822 | 0.6881 | 0.7352 |
| 0.3871 | 14.0 | 19908 | 0.4801 | 0.7815 | 0.7050 | 0.7433 |
| 0.3714 | 15.0 | 21330 | 0.4846 | 0.7827 | 0.7111 | 0.7469 |
| 0.3556 | 16.0 | 22752 | 0.4726 | 0.7906 | 0.7104 | 0.7505 |
| 0.341 | 17.0 | 24174 | 0.4787 | 0.7942 | 0.7047 | 0.7494 |
| 0.3269 | 18.0 | 25596 | 0.4914 | 0.7884 | 0.7198 | 0.7541 |
| 0.3127 | 19.0 | 27018 | 0.4774 | 0.7950 | 0.7156 | 0.7553 |
| 0.3 | 20.0 | 28440 | 0.4862 | 0.7965 | 0.7253 | 0.7609 |
| 0.2885 | 21.0 | 29862 | 0.4982 | 0.7939 | 0.7300 | 0.7620 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
