distilbert_add_GLUE_Experiment_mrpc_256
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: 0.5932
- Accuracy: 0.7108
- F1: 0.8234
- Combined Score: 0.7671
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 |
|---|---|---|---|---|---|---|
| 0.637 | 1.0 | 15 | 0.6242 | 0.6838 | 0.8122 | 0.7480 |
| 0.629 | 2.0 | 30 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6302 | 3.0 | 45 | 0.6248 | 0.6838 | 0.8122 | 0.7480 |
| 0.63 | 4.0 | 60 | 0.6241 | 0.6838 | 0.8122 | 0.7480 |
| 0.6323 | 5.0 | 75 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6299 | 6.0 | 90 | 0.6243 | 0.6838 | 0.8122 | 0.7480 |
| 0.6325 | 7.0 | 105 | 0.6239 | 0.6838 | 0.8122 | 0.7480 |
| 0.6301 | 8.0 | 120 | 0.6239 | 0.6838 | 0.8122 | 0.7480 |
| 0.6324 | 9.0 | 135 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6293 | 10.0 | 150 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6307 | 11.0 | 165 | 0.6239 | 0.6838 | 0.8122 | 0.7480 |
| 0.6302 | 12.0 | 180 | 0.6240 | 0.6838 | 0.8122 | 0.7480 |
| 0.6338 | 13.0 | 195 | 0.6237 | 0.6838 | 0.8122 | 0.7480 |
| 0.6281 | 14.0 | 210 | 0.6225 | 0.6838 | 0.8122 | 0.7480 |
| 0.6263 | 15.0 | 225 | 0.6183 | 0.6838 | 0.8122 | 0.7480 |
| 0.6017 | 16.0 | 240 | 0.5932 | 0.7108 | 0.8234 | 0.7671 |
| 0.5213 | 17.0 | 255 | 0.6146 | 0.6642 | 0.7540 | 0.7091 |
| 0.4383 | 18.0 | 270 | 0.6405 | 0.6912 | 0.7842 | 0.7377 |
| 0.3903 | 19.0 | 285 | 0.6910 | 0.6912 | 0.7872 | 0.7392 |
| 0.363 | 20.0 | 300 | 0.7221 | 0.6544 | 0.7374 | 0.6959 |
| 0.3306 | 21.0 | 315 | 0.7583 | 0.6863 | 0.7808 | 0.7335 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2