<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
distilbert_add_GLUE_Experiment_logit_kd_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: 0.7229
- Accuracy: 0.6349
- F1: 0.0187
- Combined Score: 0.3268
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 |
|---|---|---|---|---|---|---|
| 0.9266 | 1.0 | 1422 | 0.8016 | 0.6318 | 0.0 | 0.3159 |
| 0.79 | 2.0 | 2844 | 0.7941 | 0.6318 | 0.0 | 0.3159 |
| 0.7769 | 3.0 | 4266 | 0.7865 | 0.6318 | 0.0 | 0.3159 |
| 0.7686 | 4.0 | 5688 | 0.8044 | 0.6318 | 0.0 | 0.3159 |
| 0.7604 | 5.0 | 7110 | 0.7942 | 0.6318 | 0.0 | 0.3159 |
| 0.7508 | 6.0 | 8532 | 0.8087 | 0.6318 | 0.0 | 0.3159 |
| 0.7395 | 7.0 | 9954 | 0.7740 | 0.6318 | 0.0 | 0.3159 |
| 0.7283 | 8.0 | 11376 | 0.7776 | 0.6318 | 0.0 | 0.3159 |
| 0.7149 | 9.0 | 12798 | 0.7925 | 0.6318 | 0.0 | 0.3159 |
| 0.7017 | 10.0 | 14220 | 0.7980 | 0.6318 | 0.0 | 0.3159 |
| 0.6888 | 11.0 | 15642 | 0.7555 | 0.6318 | 0.0 | 0.3159 |
| 0.6762 | 12.0 | 17064 | 0.7617 | 0.6318 | 0.0 | 0.3159 |
| 0.6651 | 13.0 | 18486 | 0.7643 | 0.6318 | 0.0 | 0.3159 |
| 0.6547 | 14.0 | 19908 | 0.7432 | 0.6318 | 0.0 | 0.3159 |
| 0.6457 | 15.0 | 21330 | 0.7386 | 0.6318 | 0.0001 | 0.3160 |
| 0.6364 | 16.0 | 22752 | 0.7638 | 0.6318 | 0.0005 | 0.3162 |
| 0.6288 | 17.0 | 24174 | 0.7437 | 0.6323 | 0.0034 | 0.3178 |
| 0.6211 | 18.0 | 25596 | 0.7229 | 0.6349 | 0.0187 | 0.3268 |
| 0.6151 | 19.0 | 27018 | 0.7449 | 0.6329 | 0.0072 | 0.3201 |
| 0.6091 | 20.0 | 28440 | 0.7420 | 0.6337 | 0.0121 | 0.3229 |
| 0.6034 | 21.0 | 29862 | 0.7284 | 0.6339 | 0.0129 | 0.3234 |
| 0.5986 | 22.0 | 31284 | 0.7301 | 0.6339 | 0.0131 | 0.3235 |
| 0.5935 | 23.0 | 32706 | 0.7277 | 0.6361 | 0.0254 | 0.3308 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
