BERT_B06
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: 0.2194
- Precision: 0.6602
- Recall: 0.7091
- F1: 0.6837
- Accuracy: 0.9351
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 |
|---|---|---|---|---|---|---|---|
| 0.4915 | 1.0 | 46 | 0.3346 | 0.5142 | 0.5049 | 0.5095 | 0.9013 |
| 0.2895 | 2.0 | 92 | 0.2445 | 0.6164 | 0.6517 | 0.6336 | 0.9235 |
| 0.1661 | 3.0 | 138 | 0.2254 | 0.6375 | 0.6937 | 0.6644 | 0.9321 |
| 0.1329 | 4.0 | 184 | 0.2213 | 0.6596 | 0.6993 | 0.6789 | 0.9331 |
| 0.1214 | 5.0 | 230 | 0.2194 | 0.6602 | 0.7091 | 0.6837 | 0.9351 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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