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distilbert-base-uncased__sst2__train-16-4
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1501
- Accuracy: 0.6387
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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 |
|---|---|---|---|---|
| 0.7043 | 1.0 | 7 | 0.7139 | 0.2857 |
| 0.68 | 2.0 | 14 | 0.7398 | 0.2857 |
| 0.641 | 3.0 | 21 | 0.7723 | 0.2857 |
| 0.5424 | 4.0 | 28 | 0.8391 | 0.2857 |
| 0.5988 | 5.0 | 35 | 0.7761 | 0.2857 |
| 0.3698 | 6.0 | 42 | 0.7707 | 0.4286 |
| 0.3204 | 7.0 | 49 | 0.8290 | 0.4286 |
| 0.2882 | 8.0 | 56 | 0.6551 | 0.5714 |
| 0.1512 | 9.0 | 63 | 0.5652 | 0.5714 |
| 0.1302 | 10.0 | 70 | 0.5278 | 0.5714 |
| 0.1043 | 11.0 | 77 | 0.4987 | 0.7143 |
| 0.0272 | 12.0 | 84 | 0.5278 | 0.5714 |
| 0.0201 | 13.0 | 91 | 0.5307 | 0.5714 |
| 0.0129 | 14.0 | 98 | 0.5382 | 0.5714 |
| 0.0117 | 15.0 | 105 | 0.5227 | 0.5714 |
| 0.0094 | 16.0 | 112 | 0.5066 | 0.7143 |
| 0.0104 | 17.0 | 119 | 0.4869 | 0.7143 |
| 0.0069 | 18.0 | 126 | 0.4786 | 0.7143 |
| 0.0062 | 19.0 | 133 | 0.4707 | 0.7143 |
| 0.0065 | 20.0 | 140 | 0.4669 | 0.7143 |
| 0.0051 | 21.0 | 147 | 0.4686 | 0.7143 |
| 0.0049 | 22.0 | 154 | 0.4784 | 0.7143 |
| 0.0046 | 23.0 | 161 | 0.4839 | 0.7143 |
| 0.0039 | 24.0 | 168 | 0.4823 | 0.7143 |
| 0.0044 | 25.0 | 175 | 0.4791 | 0.7143 |
| 0.0037 | 26.0 | 182 | 0.4778 | 0.7143 |
| 0.0038 | 27.0 | 189 | 0.4770 | 0.7143 |
| 0.0036 | 28.0 | 196 | 0.4750 | 0.7143 |
| 0.0031 | 29.0 | 203 | 0.4766 | 0.7143 |
| 0.0031 | 30.0 | 210 | 0.4754 | 0.7143 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
