bert-keyword-extractor
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1341
- Precision: 0.8565
- Recall: 0.8874
- Accuracy: 0.9738
- F1: 0.8717
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
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|---|---|---|---|---|---|---|---|
| 0.1688 | 1.0 | 1875 | 0.1233 | 0.7194 | 0.7738 | 0.9501 | 0.7456 |
| 0.1219 | 2.0 | 3750 | 0.1014 | 0.7724 | 0.8166 | 0.9606 | 0.7939 |
| 0.0834 | 3.0 | 5625 | 0.0977 | 0.8280 | 0.8263 | 0.9672 | 0.8272 |
| 0.0597 | 4.0 | 7500 | 0.0984 | 0.8304 | 0.8680 | 0.9704 | 0.8488 |
| 0.0419 | 5.0 | 9375 | 0.1042 | 0.8417 | 0.8687 | 0.9717 | 0.8550 |
| 0.0315 | 6.0 | 11250 | 0.1161 | 0.8520 | 0.8839 | 0.9729 | 0.8677 |
| 0.0229 | 7.0 | 13125 | 0.1282 | 0.8469 | 0.8939 | 0.9734 | 0.8698 |
| 0.0182 | 8.0 | 15000 | 0.1341 | 0.8565 | 0.8874 | 0.9738 | 0.8717 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1