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distilbert_sa_GLUE_Experiment_mrpc_96
This model is a fine-tuned version of distilbert-base-uncased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.5873
- Accuracy: 0.6887
- F1: 0.7829
- Combined Score: 0.7358
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 |
|---|---|---|---|---|---|---|
| 0.6677 | 1.0 | 15 | 0.6479 | 0.6838 | 0.8122 | 0.7480 |
| 0.6455 | 2.0 | 30 | 0.6395 | 0.6838 | 0.8122 | 0.7480 |
| 0.6399 | 3.0 | 45 | 0.6331 | 0.6838 | 0.8122 | 0.7480 |
| 0.6361 | 4.0 | 60 | 0.6288 | 0.6838 | 0.8122 | 0.7480 |
| 0.6352 | 5.0 | 75 | 0.6262 | 0.6838 | 0.8122 | 0.7480 |
| 0.6315 | 6.0 | 90 | 0.6252 | 0.6838 | 0.8122 | 0.7480 |
| 0.6331 | 7.0 | 105 | 0.6244 | 0.6838 | 0.8122 | 0.7480 |
| 0.6292 | 8.0 | 120 | 0.6242 | 0.6838 | 0.8122 | 0.7480 |
| 0.6314 | 9.0 | 135 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6296 | 10.0 | 150 | 0.6242 | 0.6838 | 0.8122 | 0.7480 |
| 0.6306 | 11.0 | 165 | 0.6241 | 0.6838 | 0.8122 | 0.7480 |
| 0.63 | 12.0 | 180 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6337 | 13.0 | 195 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6299 | 14.0 | 210 | 0.6239 | 0.6838 | 0.8122 | 0.7480 |
| 0.6297 | 15.0 | 225 | 0.6230 | 0.6838 | 0.8122 | 0.7480 |
| 0.6248 | 16.0 | 240 | 0.6187 | 0.6838 | 0.8122 | 0.7480 |
| 0.6065 | 17.0 | 255 | 0.5999 | 0.6936 | 0.8164 | 0.7550 |
| 0.5624 | 18.0 | 270 | 0.6007 | 0.6838 | 0.7659 | 0.7249 |
| 0.5185 | 19.0 | 285 | 0.5891 | 0.6838 | 0.7772 | 0.7305 |
| 0.4664 | 20.0 | 300 | 0.5873 | 0.6887 | 0.7829 | 0.7358 |
| 0.4248 | 21.0 | 315 | 0.5893 | 0.6936 | 0.7764 | 0.7350 |
| 0.3844 | 22.0 | 330 | 0.5949 | 0.7010 | 0.7798 | 0.7404 |
| 0.3551 | 23.0 | 345 | 0.5942 | 0.7034 | 0.7866 | 0.7450 |
| 0.3314 | 24.0 | 360 | 0.6040 | 0.7034 | 0.7881 | 0.7458 |
| 0.3181 | 25.0 | 375 | 0.6162 | 0.7010 | 0.7867 | 0.7438 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
