BERT_B03
This model is a fine-tuned version of distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4794
- Precision: 0.5770
- Recall: 0.5997
- F1: 0.5881
- Accuracy: 0.8676
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: 4e-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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.5733 | 1.0 | 72 | 0.5985 | 0.4893 | 0.5053 | 0.4972 | 0.8280 |
| 0.4441 | 2.0 | 144 | 0.4892 | 0.5632 | 0.5554 | 0.5593 | 0.8567 |
| 0.3301 | 3.0 | 216 | 0.4655 | 0.5494 | 0.5751 | 0.5619 | 0.8638 |
| 0.2906 | 4.0 | 288 | 0.4825 | 0.5608 | 0.6013 | 0.5804 | 0.8653 |
| 0.2309 | 5.0 | 360 | 0.4794 | 0.5770 | 0.5997 | 0.5881 | 0.8676 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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