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distilbert_add_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: 0.6704
- Accuracy: 0.6417
- F1: 0.0575
- Combined Score: 0.3496
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 |
|---|---|---|---|---|---|---|
| 0.8489 | 1.0 | 1422 | 0.8086 | 0.6318 | 0.0 | 0.3159 |
| 0.776 | 2.0 | 2844 | 0.7797 | 0.6318 | 0.0 | 0.3159 |
| 0.7501 | 3.0 | 4266 | 0.7462 | 0.6318 | 0.0 | 0.3159 |
| 0.7008 | 4.0 | 5688 | 0.7089 | 0.6318 | 0.0 | 0.3159 |
| 0.6547 | 5.0 | 7110 | 0.7026 | 0.6318 | 0.0 | 0.3159 |
| 0.6221 | 6.0 | 8532 | 0.6962 | 0.6338 | 0.0115 | 0.3226 |
| 0.5981 | 7.0 | 9954 | 0.6812 | 0.6437 | 0.0693 | 0.3565 |
| 0.5797 | 8.0 | 11376 | 0.6846 | 0.6361 | 0.0261 | 0.3311 |
| 0.565 | 9.0 | 12798 | 0.6835 | 0.6423 | 0.0609 | 0.3516 |
| 0.5537 | 10.0 | 14220 | 0.6816 | 0.6516 | 0.1130 | 0.3823 |
| 0.5446 | 11.0 | 15642 | 0.6907 | 0.6392 | 0.0427 | 0.3409 |
| 0.5368 | 12.0 | 17064 | 0.6788 | 0.6476 | 0.0921 | 0.3699 |
| 0.5305 | 13.0 | 18486 | 0.6729 | 0.6531 | 0.1216 | 0.3874 |
| 0.525 | 14.0 | 19908 | 0.6704 | 0.6417 | 0.0575 | 0.3496 |
| 0.5206 | 15.0 | 21330 | 0.6757 | 0.6467 | 0.0873 | 0.3670 |
| 0.5165 | 16.0 | 22752 | 0.6805 | 0.6481 | 0.0940 | 0.3711 |
| 0.513 | 17.0 | 24174 | 0.6760 | 0.6474 | 0.0901 | 0.3688 |
| 0.5103 | 18.0 | 25596 | 0.6767 | 0.6505 | 0.1071 | 0.3788 |
| 0.5074 | 19.0 | 27018 | 0.6798 | 0.6486 | 0.0964 | 0.3725 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
