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bert-base-multilingual-cased-VITD
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4876
- Accuracy: 0.7338
- F1 score: 0.7300
Training and evaluation data
banglaVITD is used for training and validation.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score |
|---|---|---|---|---|---|
| 0.9505 | 1.0 | 169 | 0.8351 | 0.6361 | 0.5587 |
| 0.7782 | 2.0 | 338 | 0.7390 | 0.7120 | 0.6829 |
| 0.5627 | 3.0 | 507 | 0.6598 | 0.7278 | 0.7265 |
| 0.4067 | 4.0 | 676 | 0.7841 | 0.7233 | 0.7239 |
| 0.2826 | 5.0 | 845 | 0.9663 | 0.7353 | 0.7265 |
| 0.219 | 6.0 | 1014 | 1.0408 | 0.7391 | 0.7294 |
| 0.1434 | 7.0 | 1183 | 1.2585 | 0.7346 | 0.7299 |
| 0.1018 | 8.0 | 1352 | 1.3435 | 0.7331 | 0.7299 |
| 0.0734 | 9.0 | 1521 | 1.4392 | 0.7346 | 0.7319 |
| 0.0558 | 10.0 | 1690 | 1.4876 | 0.7338 | 0.7300 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
