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bert-finetuned-tmvar-corpus
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0002
- I F1: 1.0
- S F1: 1.0
- M F1: 0.9994
- P F1: 0.9994
- R F1: 1.0
- D F1: 1.0
- T F1: 1.0
- F F1: 1.0
- W F1: 0.9986
- A F1: 0.9977
- Precision: 0.9988
- Recall: 0.9997
- F1: 0.9993
- Accuracy: 1.0000
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | I F1 | S F1 | M F1 | P F1 | R F1 | D F1 | T F1 | F F1 | W F1 | A F1 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0364 | 1.0 | 454 | 0.0250 | 0.7336 | 0.0 | 0.8644 | 0.8347 | 0.9246 | 0.0 | 0.8854 | 0.0 | 0.9041 | 0.9774 | 0.8595 | 0.8618 | 0.8607 | 0.9927 |
| 0.0371 | 2.0 | 908 | 0.0132 | 0.7906 | 0.0 | 0.9410 | 0.8785 | 0.9947 | 0.0 | 0.9509 | 0.4118 | 0.9615 | 0.9931 | 0.9068 | 0.9236 | 0.9151 | 0.9960 |
| 0.0084 | 3.0 | 1362 | 0.0074 | 0.8591 | 0.8 | 0.9683 | 0.9585 | 0.9894 | 0.0 | 0.9677 | 0.8636 | 0.9719 | 0.9954 | 0.9463 | 0.9635 | 0.9548 | 0.9977 |
| 0.0034 | 4.0 | 1816 | 0.0040 | 0.9205 | 0.9412 | 0.9815 | 0.9735 | 1.0 | 0.8 | 0.9656 | 0.9778 | 0.9830 | 0.9977 | 0.9636 | 0.9818 | 0.9726 | 0.9987 |
| 0.0029 | 5.0 | 2270 | 0.0022 | 0.9623 | 0.9412 | 0.9864 | 0.9836 | 0.9947 | 0.8 | 0.9837 | 0.9333 | 0.9859 | 0.9977 | 0.9793 | 0.9865 | 0.9829 | 0.9992 |
| 0.003 | 6.0 | 2724 | 0.0024 | 0.9748 | 0.9412 | 0.9797 | 0.9789 | 1.0 | 0.8 | 0.9887 | 0.9333 | 0.9879 | 0.9954 | 0.9748 | 0.9903 | 0.9825 | 0.9991 |
| 0.0009 | 7.0 | 3178 | 0.0008 | 0.9874 | 1.0 | 0.9936 | 0.9951 | 1.0 | 1.0 | 0.9975 | 1.0 | 0.9936 | 0.9977 | 0.9927 | 0.9959 | 0.9943 | 0.9997 |
| 0.0002 | 8.0 | 3632 | 0.0004 | 1.0 | 1.0 | 0.9955 | 0.9976 | 1.0 | 1.0 | 0.9987 | 1.0 | 0.9964 | 0.9977 | 0.9956 | 0.9991 | 0.9974 | 0.9999 |
| 0.0004 | 9.0 | 4086 | 0.0003 | 1.0 | 1.0 | 0.9994 | 0.9994 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9979 | 0.9977 | 0.9985 | 0.9997 | 0.9991 | 1.0000 |
| 0.0007 | 10.0 | 4540 | 0.0002 | 1.0 | 1.0 | 0.9994 | 0.9994 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9986 | 0.9977 | 0.9988 | 0.9997 | 0.9993 | 1.0000 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
