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bert-finetuned-requirements
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0538
- Precision: 0.9609
- Recall: 0.9609
- F1: 0.9609
- Accuracy: 0.9822
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 33 | 0.1992 | 0.8903 | 0.8880 | 0.8892 | 0.9476 |
| No log | 2.0 | 66 | 0.1351 | 0.9039 | 0.9062 | 0.9051 | 0.9554 |
| No log | 3.0 | 99 | 0.0949 | 0.9368 | 0.9271 | 0.9319 | 0.9677 |
| No log | 4.0 | 132 | 0.0613 | 0.9556 | 0.9531 | 0.9544 | 0.9788 |
| No log | 5.0 | 165 | 0.0538 | 0.9609 | 0.9609 | 0.9609 | 0.9822 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
