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--- |
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license: gpl-3.0 |
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tags: |
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- generated_from_trainer |
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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_0301_J_DATA |
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results: [] |
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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_0301_J_DATA |
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This model is a fine-tuned version of [ckiplab/bert-base-chinese-ner](https://huggingface.co/ckiplab/bert-base-chinese-ner) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0318 |
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- Precision: 0.9551 |
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- Recall: 0.9787 |
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- F1: 0.9668 |
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- Accuracy: 0.9923 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 15 |
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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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| 0.352 | 1.0 | 705 | 0.0754 | 0.8558 | 0.9182 | 0.8859 | 0.9774 | |
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| 0.0636 | 2.0 | 1410 | 0.0928 | 0.9082 | 0.9428 | 0.9252 | 0.9794 | |
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| 0.025 | 3.0 | 2115 | 0.0576 | 0.9262 | 0.9574 | 0.9416 | 0.9828 | |
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| 0.0253 | 4.0 | 2820 | 0.0801 | 0.9419 | 0.9630 | 0.9523 | 0.9824 | |
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| 0.0169 | 5.0 | 3525 | 0.0400 | 0.9287 | 0.9641 | 0.9461 | 0.9886 | |
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| 0.0108 | 6.0 | 4230 | 0.0370 | 0.9372 | 0.9709 | 0.9537 | 0.9903 | |
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| 0.0143 | 7.0 | 4935 | 0.0430 | 0.9308 | 0.9652 | 0.9477 | 0.9855 | |
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| 0.0083 | 8.0 | 5640 | 0.0648 | 0.9382 | 0.9709 | 0.9543 | 0.9877 | |
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| 0.0057 | 9.0 | 6345 | 0.0269 | 0.9222 | 0.9697 | 0.9454 | 0.9903 | |
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| 0.0036 | 10.0 | 7050 | 0.0338 | 0.9464 | 0.9697 | 0.9579 | 0.9927 | |
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| 0.003 | 11.0 | 7755 | 0.0486 | 0.9581 | 0.9742 | 0.9661 | 0.9894 | |
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| 0.0017 | 12.0 | 8460 | 0.0230 | 0.9593 | 0.9765 | 0.9678 | 0.9909 | |
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| 0.001 | 13.0 | 9165 | 0.0260 | 0.9508 | 0.9753 | 0.9629 | 0.9949 | |
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| 0.0014 | 14.0 | 9870 | 0.0357 | 0.9582 | 0.9765 | 0.9672 | 0.9914 | |
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| 0.0008 | 15.0 | 10575 | 0.0318 | 0.9551 | 0.9787 | 0.9668 | 0.9923 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.12.1 |
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