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distilbert_sa_GLUE_Experiment_data_aug_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: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
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 |
|---|---|---|---|---|---|---|
| 0.3242 | 1.0 | 980 | 0.0830 | 0.9804 | 0.9857 | 0.9830 |
| 0.0843 | 2.0 | 1960 | 0.0355 | 0.9828 | 0.9875 | 0.9852 |
| 0.0431 | 3.0 | 2940 | 0.0105 | 1.0 | 1.0 | 1.0 |
| 0.0268 | 4.0 | 3920 | 0.0046 | 1.0 | 1.0 | 1.0 |
| 0.019 | 5.0 | 4900 | 0.0015 | 1.0 | 1.0 | 1.0 |
| 0.0141 | 6.0 | 5880 | 0.0011 | 1.0 | 1.0 | 1.0 |
| 0.0115 | 7.0 | 6860 | 0.0007 | 1.0 | 1.0 | 1.0 |
| 0.0094 | 8.0 | 7840 | 0.0004 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 9.0 | 8820 | 0.0004 | 1.0 | 1.0 | 1.0 |
| 0.0056 | 10.0 | 9800 | 0.0006 | 1.0 | 1.0 | 1.0 |
| 0.0056 | 11.0 | 10780 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0039 | 12.0 | 11760 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0038 | 13.0 | 12740 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0029 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0026 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0025 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0019 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 18.0 | 17640 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.001 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 27.0 | 26460 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 30.0 | 29400 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 31.0 | 30380 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 32.0 | 31360 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 33.0 | 32340 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 34.0 | 33320 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 35.0 | 34300 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 36.0 | 35280 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 37.0 | 36260 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 38.0 | 37240 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 39.0 | 38220 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 40.0 | 39200 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 41.0 | 40180 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 42.0 | 41160 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 43.0 | 42140 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 44.0 | 43120 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 45.0 | 44100 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 46.0 | 45080 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 47.0 | 46060 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 48.0 | 47040 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 49.0 | 48020 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 50.0 | 49000 | 0.0000 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
