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bert-base-cased-NER-favsbot
This model is a fine-tuned version of bert-base-cased on the favsbot dataset. It achieves the following results on the evaluation set:
- Loss: 0.1680
- Precision: 0.8462
- Recall: 0.88
- F1: 0.8627
- Accuracy: 0.9444
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: 1.5e-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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 7 | 1.8761 | 0.0 | 0.0 | 0.0 | 0.5833 |
| No log | 2.0 | 14 | 1.3530 | 0.0 | 0.0 | 0.0 | 0.5972 |
| No log | 3.0 | 21 | 1.0400 | 1.0 | 0.12 | 0.2143 | 0.6389 |
| No log | 4.0 | 28 | 0.7987 | 0.7895 | 0.6 | 0.6818 | 0.8194 |
| No log | 5.0 | 35 | 0.6055 | 0.85 | 0.68 | 0.7556 | 0.875 |
| No log | 6.0 | 42 | 0.4749 | 0.8696 | 0.8 | 0.8333 | 0.9167 |
| No log | 7.0 | 49 | 0.3838 | 0.84 | 0.84 | 0.8400 | 0.9444 |
| No log | 8.0 | 56 | 0.3084 | 0.88 | 0.88 | 0.88 | 0.9583 |
| No log | 9.0 | 63 | 0.2643 | 0.88 | 0.88 | 0.88 | 0.9583 |
| No log | 10.0 | 70 | 0.2360 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
| No log | 11.0 | 77 | 0.2168 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
| No log | 12.0 | 84 | 0.2031 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
| No log | 13.0 | 91 | 0.1937 | 0.88 | 0.88 | 0.88 | 0.9583 |
| No log | 14.0 | 98 | 0.1853 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
| No log | 15.0 | 105 | 0.1791 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
| No log | 16.0 | 112 | 0.1757 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
| No log | 17.0 | 119 | 0.1718 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
| No log | 18.0 | 126 | 0.1698 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 19.0 | 133 | 0.1686 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 20.0 | 140 | 0.1680 | 0.8462 | 0.88 | 0.8627 | 0.9444 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
