bert-base-fine-tuned-text-classificarion-ds-dropout
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0721
- F1: 0.7307
- Recall: 0.7499
- Accuracy: 0.7499
- Precision: 0.7427
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Recall | Accuracy | Precision |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 442 | 2.6972 | 0.4056 | 0.4819 | 0.4819 | 0.4782 |
| 3.5527 | 2.0 | 884 | 1.6292 | 0.5981 | 0.6559 | 0.6559 | 0.6035 |
| 2.1075 | 3.0 | 1326 | 1.2669 | 0.6801 | 0.7117 | 0.7117 | 0.6923 |
| 1.2767 | 4.0 | 1768 | 1.0995 | 0.7133 | 0.7437 | 0.7437 | 0.7336 |
| 0.9148 | 5.0 | 2210 | 1.0721 | 0.7307 | 0.7499 | 0.7499 | 0.7427 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Base model
google-bert/bert-base-multilingual-cased