bert-small-finetuned-finetuned
This model is a fine-tuned version of ryantaw/bert-small-finetuned on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0767
- Accuracy: 0.6119
- F1 Score: 0.6156
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: 86
- eval_batch_size: 86
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| 0.7125 | 1.0 | 18 | 1.0136 | 0.6011 | 0.5997 |
| 0.604 | 2.0 | 36 | 1.0198 | 0.6038 | 0.6058 |
| 0.5421 | 3.0 | 54 | 1.0517 | 0.6065 | 0.6068 |
| 0.4724 | 4.0 | 72 | 1.0767 | 0.6119 | 0.6156 |
| 0.42 | 5.0 | 90 | 1.1184 | 0.5768 | 0.5751 |
| 0.3823 | 6.0 | 108 | 1.1217 | 0.5876 | 0.5881 |
| 0.3312 | 7.0 | 126 | 1.1425 | 0.6065 | 0.6053 |
| 0.3045 | 8.0 | 144 | 1.1760 | 0.6065 | 0.6095 |
| 0.2662 | 9.0 | 162 | 1.2044 | 0.6065 | 0.6090 |
| 0.2403 | 10.0 | 180 | 1.2143 | 0.6011 | 0.6011 |
| 0.2308 | 11.0 | 198 | 1.2394 | 0.5903 | 0.5927 |
| 0.2053 | 12.0 | 216 | 1.2589 | 0.6038 | 0.6068 |
| 0.1808 | 13.0 | 234 | 1.2895 | 0.6065 | 0.6071 |
| 0.1599 | 14.0 | 252 | 1.3144 | 0.6065 | 0.6086 |
| 0.1497 | 15.0 | 270 | 1.3386 | 0.5930 | 0.5951 |
| 0.1383 | 16.0 | 288 | 1.3608 | 0.5903 | 0.5931 |
| 0.1321 | 17.0 | 306 | 1.3624 | 0.5876 | 0.5888 |
| 0.1183 | 18.0 | 324 | 1.3810 | 0.5930 | 0.5945 |
| 0.1196 | 19.0 | 342 | 1.3827 | 0.5903 | 0.5927 |
| 0.1181 | 20.0 | 360 | 1.3805 | 0.5903 | 0.5920 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1