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bert-base-chinese-finetuned-ner-v1
This model is a fine-tuned version of bert-base-chinese on the fdner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0413
- Precision: 0.9812
- Recall: 0.9886
- F1: 0.9849
- Accuracy: 0.9910
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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 8 | 2.0640 | 0.0 | 0.0 | 0.0 | 0.4323 |
| No log | 2.0 | 16 | 1.7416 | 0.0204 | 0.0227 | 0.0215 | 0.5123 |
| No log | 3.0 | 24 | 1.5228 | 0.0306 | 0.0265 | 0.0284 | 0.5456 |
| No log | 4.0 | 32 | 1.2597 | 0.0961 | 0.1591 | 0.1198 | 0.6491 |
| No log | 5.0 | 40 | 1.0273 | 0.1588 | 0.2159 | 0.1830 | 0.7450 |
| No log | 6.0 | 48 | 0.8026 | 0.2713 | 0.3258 | 0.2960 | 0.8208 |
| No log | 7.0 | 56 | 0.6547 | 0.36 | 0.4091 | 0.3830 | 0.8513 |
| No log | 8.0 | 64 | 0.5180 | 0.4650 | 0.5038 | 0.4836 | 0.8873 |
| No log | 9.0 | 72 | 0.4318 | 0.5139 | 0.5606 | 0.5362 | 0.9067 |
| No log | 10.0 | 80 | 0.3511 | 0.6169 | 0.6894 | 0.6512 | 0.9291 |
| No log | 11.0 | 88 | 0.2887 | 0.6691 | 0.6894 | 0.6791 | 0.9414 |
| No log | 12.0 | 96 | 0.2396 | 0.7042 | 0.7576 | 0.7299 | 0.9516 |
| No log | 13.0 | 104 | 0.2052 | 0.7568 | 0.8371 | 0.7950 | 0.9587 |
| No log | 14.0 | 112 | 0.1751 | 0.8303 | 0.8712 | 0.8503 | 0.9610 |
| No log | 15.0 | 120 | 0.1512 | 0.8464 | 0.8977 | 0.8713 | 0.9668 |
| No log | 16.0 | 128 | 0.1338 | 0.8759 | 0.9091 | 0.8922 | 0.9710 |
| No log | 17.0 | 136 | 0.1147 | 0.8959 | 0.9129 | 0.9043 | 0.9746 |
| No log | 18.0 | 144 | 0.1011 | 0.9326 | 0.9432 | 0.9379 | 0.9761 |
| No log | 19.0 | 152 | 0.0902 | 0.9251 | 0.9356 | 0.9303 | 0.9795 |
| No log | 20.0 | 160 | 0.0806 | 0.9440 | 0.9583 | 0.9511 | 0.9804 |
| No log | 21.0 | 168 | 0.0743 | 0.9586 | 0.9659 | 0.9623 | 0.9812 |
| No log | 22.0 | 176 | 0.0649 | 0.9511 | 0.9583 | 0.9547 | 0.9851 |
| No log | 23.0 | 184 | 0.0595 | 0.9591 | 0.9773 | 0.9681 | 0.9876 |
| No log | 24.0 | 192 | 0.0537 | 0.9625 | 0.9735 | 0.9680 | 0.9883 |
| No log | 25.0 | 200 | 0.0505 | 0.9701 | 0.9848 | 0.9774 | 0.9894 |
| No log | 26.0 | 208 | 0.0464 | 0.9737 | 0.9811 | 0.9774 | 0.9904 |
| No log | 27.0 | 216 | 0.0439 | 0.9737 | 0.9811 | 0.9774 | 0.9906 |
| No log | 28.0 | 224 | 0.0428 | 0.9812 | 0.9886 | 0.9849 | 0.9910 |
| No log | 29.0 | 232 | 0.0417 | 0.9812 | 0.9886 | 0.9849 | 0.9910 |
| No log | 30.0 | 240 | 0.0413 | 0.9812 | 0.9886 | 0.9849 | 0.9910 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
