BERT_B05
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: 0.3273
- Precision: 0.6078
- Recall: 0.6527
- F1: 0.6295
- Accuracy: 0.9042
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 |
|---|---|---|---|---|---|---|---|
| 0.4541 | 1.0 | 92 | 0.3712 | 0.5576 | 0.5232 | 0.5399 | 0.8877 |
| 0.3098 | 2.0 | 184 | 0.3191 | 0.5546 | 0.6146 | 0.5831 | 0.8974 |
| 0.2016 | 3.0 | 276 | 0.3065 | 0.6049 | 0.6367 | 0.6204 | 0.9026 |
| 0.1919 | 4.0 | 368 | 0.3161 | 0.6148 | 0.6443 | 0.6292 | 0.9064 |
| 0.1387 | 5.0 | 460 | 0.3273 | 0.6078 | 0.6527 | 0.6295 | 0.9042 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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