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silviacamplani/distilbert-finetuned-ner-music
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.6767
- Validation Loss: 0.7802
- Train Precision: 0.5256
- Train Recall: 0.5824
- Train F1: 0.5525
- Train Accuracy: 0.8017
- Epoch: 9
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:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 370, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 2.6671 | 2.0032 | 0.0 | 0.0 | 0.0 | 0.5482 | 0 |
| 1.7401 | 1.5194 | 0.1820 | 0.0693 | 0.1004 | 0.5902 | 1 |
| 1.3487 | 1.2627 | 0.2628 | 0.2952 | 0.2781 | 0.6766 | 2 |
| 1.1390 | 1.0990 | 0.4018 | 0.4527 | 0.4257 | 0.7181 | 3 |
| 0.9823 | 0.9837 | 0.4575 | 0.4887 | 0.4726 | 0.7311 | 4 |
| 0.8741 | 0.9022 | 0.5008 | 0.5338 | 0.5168 | 0.7544 | 5 |
| 0.7904 | 0.8449 | 0.5085 | 0.5626 | 0.5342 | 0.7776 | 6 |
| 0.7327 | 0.8097 | 0.5211 | 0.5779 | 0.5480 | 0.7917 | 7 |
| 0.7000 | 0.7872 | 0.5281 | 0.5842 | 0.5547 | 0.7975 | 8 |
| 0.6767 | 0.7802 | 0.5256 | 0.5824 | 0.5525 | 0.8017 | 9 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
