distilbert_add_GLUE_Experiment_logit_kd_qqp_384
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: 0.6543
- Accuracy: 0.6542
- F1: 0.1220
- Combined Score: 0.3881
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 |
|---|---|---|---|---|---|---|
| 0.8184 | 1.0 | 1422 | 0.7776 | 0.6318 | 0.0 | 0.3159 |
| 0.7189 | 2.0 | 2844 | 0.6932 | 0.6333 | 0.0083 | 0.3208 |
| 0.6368 | 3.0 | 4266 | 0.6602 | 0.6326 | 0.0044 | 0.3185 |
| 0.5938 | 4.0 | 5688 | 0.6756 | 0.6516 | 0.1109 | 0.3812 |
| 0.5663 | 5.0 | 7110 | 0.6744 | 0.6398 | 0.0459 | 0.3428 |
| 0.5478 | 6.0 | 8532 | 0.6685 | 0.6479 | 0.0888 | 0.3684 |
| 0.5345 | 7.0 | 9954 | 0.6543 | 0.6542 | 0.1220 | 0.3881 |
| 0.5241 | 8.0 | 11376 | 0.6601 | 0.6469 | 0.0848 | 0.3659 |
| 0.5161 | 9.0 | 12798 | 0.6774 | 0.6590 | 0.1482 | 0.4036 |
| 0.5099 | 10.0 | 14220 | 0.6590 | 0.6610 | 0.1594 | 0.4102 |
| 0.5048 | 11.0 | 15642 | 0.6704 | 0.6529 | 0.1166 | 0.3847 |
| 0.5001 | 12.0 | 17064 | 0.6791 | 0.6562 | 0.1347 | 0.3955 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2