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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fdner |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-base-chinese-finetuned-ner-v1 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: fdner |
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type: fdner |
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args: fdner |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.981203007518797 |
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- name: Recall |
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type: recall |
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value: 0.9886363636363636 |
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- name: F1 |
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type: f1 |
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value: 0.9849056603773584 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9909536373916321 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-chinese-finetuned-ner-v1 |
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This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0413 |
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- Precision: 0.9812 |
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- Recall: 0.9886 |
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- F1: 0.9849 |
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- Accuracy: 0.9910 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 10 |
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- eval_batch_size: 10 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 8 | 2.0640 | 0.0 | 0.0 | 0.0 | 0.4323 | |
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| No log | 2.0 | 16 | 1.7416 | 0.0204 | 0.0227 | 0.0215 | 0.5123 | |
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| No log | 3.0 | 24 | 1.5228 | 0.0306 | 0.0265 | 0.0284 | 0.5456 | |
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| No log | 4.0 | 32 | 1.2597 | 0.0961 | 0.1591 | 0.1198 | 0.6491 | |
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| No log | 5.0 | 40 | 1.0273 | 0.1588 | 0.2159 | 0.1830 | 0.7450 | |
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| No log | 6.0 | 48 | 0.8026 | 0.2713 | 0.3258 | 0.2960 | 0.8208 | |
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| No log | 7.0 | 56 | 0.6547 | 0.36 | 0.4091 | 0.3830 | 0.8513 | |
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| No log | 8.0 | 64 | 0.5180 | 0.4650 | 0.5038 | 0.4836 | 0.8873 | |
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| No log | 9.0 | 72 | 0.4318 | 0.5139 | 0.5606 | 0.5362 | 0.9067 | |
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| No log | 10.0 | 80 | 0.3511 | 0.6169 | 0.6894 | 0.6512 | 0.9291 | |
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| No log | 11.0 | 88 | 0.2887 | 0.6691 | 0.6894 | 0.6791 | 0.9414 | |
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| No log | 12.0 | 96 | 0.2396 | 0.7042 | 0.7576 | 0.7299 | 0.9516 | |
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| No log | 13.0 | 104 | 0.2052 | 0.7568 | 0.8371 | 0.7950 | 0.9587 | |
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| No log | 14.0 | 112 | 0.1751 | 0.8303 | 0.8712 | 0.8503 | 0.9610 | |
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| No log | 15.0 | 120 | 0.1512 | 0.8464 | 0.8977 | 0.8713 | 0.9668 | |
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| No log | 16.0 | 128 | 0.1338 | 0.8759 | 0.9091 | 0.8922 | 0.9710 | |
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| No log | 17.0 | 136 | 0.1147 | 0.8959 | 0.9129 | 0.9043 | 0.9746 | |
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| No log | 18.0 | 144 | 0.1011 | 0.9326 | 0.9432 | 0.9379 | 0.9761 | |
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| No log | 19.0 | 152 | 0.0902 | 0.9251 | 0.9356 | 0.9303 | 0.9795 | |
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| No log | 20.0 | 160 | 0.0806 | 0.9440 | 0.9583 | 0.9511 | 0.9804 | |
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| No log | 21.0 | 168 | 0.0743 | 0.9586 | 0.9659 | 0.9623 | 0.9812 | |
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| No log | 22.0 | 176 | 0.0649 | 0.9511 | 0.9583 | 0.9547 | 0.9851 | |
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| No log | 23.0 | 184 | 0.0595 | 0.9591 | 0.9773 | 0.9681 | 0.9876 | |
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| No log | 24.0 | 192 | 0.0537 | 0.9625 | 0.9735 | 0.9680 | 0.9883 | |
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| No log | 25.0 | 200 | 0.0505 | 0.9701 | 0.9848 | 0.9774 | 0.9894 | |
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| No log | 26.0 | 208 | 0.0464 | 0.9737 | 0.9811 | 0.9774 | 0.9904 | |
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| No log | 27.0 | 216 | 0.0439 | 0.9737 | 0.9811 | 0.9774 | 0.9906 | |
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| No log | 28.0 | 224 | 0.0428 | 0.9812 | 0.9886 | 0.9849 | 0.9910 | |
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| No log | 29.0 | 232 | 0.0417 | 0.9812 | 0.9886 | 0.9849 | 0.9910 | |
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| No log | 30.0 | 240 | 0.0413 | 0.9812 | 0.9886 | 0.9849 | 0.9910 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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