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bert-base-chinese-finetuned-ner_0220_J_ORIDATA_FULL_NOMOD

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update model card README.md

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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: 0.3786
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- - Precision: 0.9357
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- - Recall: 0.9657
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- - F1: 0.9504
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- - Accuracy: 0.9577
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  ## Model description
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@@ -48,22 +48,24 @@ The following hyperparameters were used during training:
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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: 10
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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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- | 0.0925 | 1.0 | 5358 | 0.2337 | 0.9246 | 0.9655 | 0.9446 | 0.9554 |
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- | 0.0787 | 2.0 | 10716 | 0.2506 | 0.9208 | 0.9588 | 0.9394 | 0.9525 |
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- | 0.0606 | 3.0 | 16074 | 0.2914 | 0.9309 | 0.9621 | 0.9462 | 0.9537 |
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- | 0.0543 | 4.0 | 21432 | 0.2792 | 0.9248 | 0.9633 | 0.9437 | 0.9553 |
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- | 0.056 | 5.0 | 26790 | 0.3064 | 0.9332 | 0.9645 | 0.9486 | 0.9563 |
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- | 0.0384 | 6.0 | 32148 | 0.3317 | 0.9347 | 0.9632 | 0.9487 | 0.9564 |
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- | 0.0265 | 7.0 | 37506 | 0.3340 | 0.9342 | 0.9667 | 0.9502 | 0.9568 |
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- | 0.03 | 8.0 | 42864 | 0.3460 | 0.9363 | 0.9641 | 0.9500 | 0.9558 |
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- | 0.0192 | 9.0 | 48222 | 0.3649 | 0.9357 | 0.9651 | 0.9501 | 0.9576 |
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- | 0.0117 | 10.0 | 53580 | 0.3786 | 0.9357 | 0.9657 | 0.9504 | 0.9577 |
 
 
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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: 0.0522
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+ - Precision: 0.9728
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+ - Recall: 0.9739
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+ - F1: 0.9733
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+ - Accuracy: 0.9954
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  ## Model description
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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: 12
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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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+ | 0.3616 | 1.0 | 705 | 0.0914 | 0.8789 | 0.9239 | 0.9008 | 0.9821 |
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+ | 0.0643 | 2.0 | 1410 | 0.0602 | 0.9242 | 0.9420 | 0.9330 | 0.9912 |
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+ | 0.0339 | 3.0 | 2115 | 0.0533 | 0.9385 | 0.9545 | 0.9465 | 0.9910 |
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+ | 0.024 | 4.0 | 2820 | 0.0558 | 0.9595 | 0.9693 | 0.9644 | 0.9932 |
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+ | 0.0145 | 5.0 | 3525 | 0.0584 | 0.9484 | 0.9614 | 0.9549 | 0.9921 |
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+ | 0.007 | 6.0 | 4230 | 0.0535 | 0.9637 | 0.9648 | 0.9642 | 0.9940 |
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+ | 0.0145 | 7.0 | 4935 | 0.0492 | 0.9573 | 0.9682 | 0.9627 | 0.9942 |
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+ | 0.0091 | 8.0 | 5640 | 0.0486 | 0.9694 | 0.9716 | 0.9705 | 0.9957 |
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+ | 0.0049 | 9.0 | 6345 | 0.0526 | 0.9727 | 0.9727 | 0.9727 | 0.9950 |
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+ | 0.0033 | 10.0 | 7050 | 0.0515 | 0.9661 | 0.9727 | 0.9694 | 0.9949 |
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+ | 0.0023 | 11.0 | 7755 | 0.0523 | 0.9661 | 0.9716 | 0.9688 | 0.9950 |
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+ | 0.0019 | 12.0 | 8460 | 0.0522 | 0.9728 | 0.9739 | 0.9733 | 0.9954 |
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  ### Framework versions