BERT_B04
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: 0.3714
- Precision: 0.6275
- Recall: 0.6448
- F1: 0.6360
- Accuracy: 0.8938
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 |
|---|---|---|---|---|---|---|---|
| 0.4347 | 1.0 | 72 | 0.4486 | 0.5788 | 0.5151 | 0.5451 | 0.8728 |
| 0.3135 | 2.0 | 144 | 0.3850 | 0.5948 | 0.6297 | 0.6117 | 0.8835 |
| 0.2528 | 3.0 | 216 | 0.3596 | 0.6178 | 0.6030 | 0.6103 | 0.8893 |
| 0.1971 | 4.0 | 288 | 0.3701 | 0.6194 | 0.6359 | 0.6275 | 0.8911 |
| 0.1585 | 5.0 | 360 | 0.3714 | 0.6275 | 0.6448 | 0.6360 | 0.8938 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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