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roberta_ner_personal_info_v2
This model is a fine-tuned version of tner/roberta-large-ontonotes5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0202
- Precision: 0.9744
- Recall: 0.9803
- F1: 0.9774
- Accuracy: 0.9956
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: 20
- eval_batch_size: 20
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1952 | 1.0 | 621 | 0.0387 | 0.9334 | 0.9414 | 0.9374 | 0.9886 |
| 0.0407 | 2.0 | 1242 | 0.0267 | 0.9587 | 0.9606 | 0.9596 | 0.9923 |
| 0.0246 | 3.0 | 1863 | 0.0230 | 0.9616 | 0.9703 | 0.9660 | 0.9936 |
| 0.0163 | 4.0 | 2484 | 0.0206 | 0.9705 | 0.9730 | 0.9717 | 0.9946 |
| 0.0097 | 5.0 | 3105 | 0.0203 | 0.9702 | 0.9747 | 0.9724 | 0.9946 |
| 0.0076 | 6.0 | 3726 | 0.0194 | 0.9717 | 0.9774 | 0.9746 | 0.9950 |
| 0.0066 | 7.0 | 4347 | 0.0193 | 0.9751 | 0.9780 | 0.9766 | 0.9954 |
| 0.0058 | 8.0 | 4968 | 0.0201 | 0.9720 | 0.9781 | 0.9750 | 0.9952 |
| 0.0044 | 9.0 | 5589 | 0.0222 | 0.9717 | 0.9751 | 0.9734 | 0.9950 |
| 0.0037 | 10.0 | 6210 | 0.0204 | 0.9736 | 0.9786 | 0.9761 | 0.9954 |
| 0.0034 | 11.0 | 6831 | 0.0192 | 0.9771 | 0.9808 | 0.9789 | 0.9958 |
| 0.0028 | 12.0 | 7452 | 0.0210 | 0.9723 | 0.9789 | 0.9756 | 0.9953 |
| 0.0025 | 13.0 | 8073 | 0.0222 | 0.9747 | 0.9792 | 0.9769 | 0.9955 |
| 0.0023 | 14.0 | 8694 | 0.0198 | 0.9771 | 0.9806 | 0.9789 | 0.9958 |
| 0.002 | 15.0 | 9315 | 0.0202 | 0.9744 | 0.9803 | 0.9774 | 0.9956 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
