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bert-base-chinese-finetuned-ner_0301_J_DATA

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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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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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@@ -54,21 +54,21 @@ The following hyperparameters were used during training:
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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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  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: 0.0764
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+ - Precision: 0.9663
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+ - Recall: 0.9708
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+ - F1: 0.9685
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+ - Accuracy: 0.9925
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3872 | 1.0 | 705 | 0.1222 | 0.9088 | 0.9311 | 0.9198 | 0.9781 |
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+ | 0.0732 | 2.0 | 1410 | 0.0642 | 0.9303 | 0.9509 | 0.9405 | 0.9900 |
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+ | 0.034 | 3.0 | 2115 | 0.0588 | 0.9616 | 0.9661 | 0.9639 | 0.9909 |
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+ | 0.0267 | 4.0 | 2820 | 0.0631 | 0.9639 | 0.9673 | 0.9656 | 0.9925 |
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+ | 0.0232 | 5.0 | 3525 | 0.0617 | 0.9630 | 0.9720 | 0.9674 | 0.9924 |
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+ | 0.017 | 6.0 | 4230 | 0.0652 | 0.9674 | 0.9708 | 0.9691 | 0.9926 |
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+ | 0.0123 | 7.0 | 4935 | 0.0573 | 0.9618 | 0.9720 | 0.9669 | 0.9923 |
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+ | 0.009 | 8.0 | 5640 | 0.0667 | 0.9651 | 0.9696 | 0.9674 | 0.9922 |
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+ | 0.0055 | 9.0 | 6345 | 0.0768 | 0.9640 | 0.9696 | 0.9668 | 0.9925 |
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+ | 0.0045 | 10.0 | 7050 | 0.0775 | 0.9662 | 0.9696 | 0.9679 | 0.9925 |
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+ | 0.004 | 11.0 | 7755 | 0.0753 | 0.9606 | 0.9685 | 0.9645 | 0.9923 |
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+ | 0.0018 | 12.0 | 8460 | 0.0735 | 0.9629 | 0.9696 | 0.9662 | 0.9925 |
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+ | 0.0019 | 13.0 | 9165 | 0.0754 | 0.9663 | 0.9708 | 0.9685 | 0.9927 |
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+ | 0.0019 | 14.0 | 9870 | 0.0760 | 0.9651 | 0.9696 | 0.9674 | 0.9925 |
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+ | 0.0013 | 15.0 | 10575 | 0.0764 | 0.9663 | 0.9708 | 0.9685 | 0.9925 |
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  ### Framework versions