temp_model
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1984
- Accuracy: 0.9369
- F1: 0.3059
- Recall: 0.2267
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall |
|---|---|---|---|---|---|---|
| No log | 1.0 | 351 | 0.1665 | 0.9401 | 0.16 | 0.0930 |
| 0.1813 | 2.0 | 702 | 0.2099 | 0.9418 | 0.1189 | 0.0640 |
| 0.1067 | 3.0 | 1053 | 0.1984 | 0.9369 | 0.3059 | 0.2267 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for AnonymousCS/temp_model
Base model
google-bert/bert-base-multilingual-cased