fine_tuned_bert
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1259
- F1: 0.8182
- F5: 0.8326
- Precision: 0.7826
- Recall: 0.8571
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F5 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 65 | 0.2964 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 130 | 0.2682 | 0.4737 | 0.4081 | 0.8182 | 0.3333 |
| No log | 3.0 | 195 | 0.2208 | 0.65 | 0.7421 | 0.4906 | 0.9630 |
| No log | 4.0 | 260 | 0.1924 | 0.7273 | 0.7816 | 0.6154 | 0.8889 |
| No log | 5.0 | 325 | 0.1246 | 0.8727 | 0.8788 | 0.8571 | 0.8889 |
| No log | 6.0 | 390 | 0.1142 | 0.8519 | 0.8519 | 0.8519 | 0.8519 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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Base model
google-bert/bert-base-multilingual-cased