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bert-base-chinese-finetuned-ner_0220_J_ORIDATA

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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: 0.5830
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- - Precision: 0.9092
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- - Recall: 0.9508
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- - F1: 0.9296
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- - Accuracy: 0.9462
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  ## Model description
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@@ -54,16 +54,16 @@ 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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- | 0.0277 | 1.0 | 884 | 0.5987 | 0.8694 | 0.9424 | 0.9044 | 0.9359 |
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- | 0.0373 | 2.0 | 1768 | 0.5396 | 0.8824 | 0.9475 | 0.9138 | 0.9452 |
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- | 0.027 | 3.0 | 2652 | 0.5509 | 0.8994 | 0.9398 | 0.9192 | 0.9459 |
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- | 0.0181 | 4.0 | 3536 | 0.5706 | 0.9006 | 0.9449 | 0.9222 | 0.9434 |
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- | 0.0144 | 5.0 | 4420 | 0.5605 | 0.9104 | 0.9466 | 0.9281 | 0.9462 |
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- | 0.011 | 6.0 | 5304 | 0.5323 | 0.8775 | 0.9466 | 0.9107 | 0.9382 |
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- | 0.0103 | 7.0 | 6188 | 0.5870 | 0.9073 | 0.9534 | 0.9298 | 0.9433 |
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- | 0.0065 | 8.0 | 7072 | 0.5986 | 0.9062 | 0.95 | 0.9276 | 0.9450 |
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- | 0.0098 | 9.0 | 7956 | 0.5794 | 0.9062 | 0.95 | 0.9276 | 0.9458 |
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- | 0.0032 | 10.0 | 8840 | 0.5830 | 0.9092 | 0.9508 | 0.9296 | 0.9462 |
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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: 0.4109
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+ - Precision: 0.9088
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+ - Recall: 0.9581
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+ - F1: 0.9328
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+ - Accuracy: 0.9478
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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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+ | 0.5095 | 1.0 | 884 | 0.2940 | 0.8565 | 0.9269 | 0.8903 | 0.9355 |
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+ | 0.2381 | 2.0 | 1768 | 0.2669 | 0.8910 | 0.9474 | 0.9184 | 0.9442 |
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+ | 0.2057 | 3.0 | 2652 | 0.2566 | 0.9011 | 0.9507 | 0.9252 | 0.9438 |
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+ | 0.1856 | 4.0 | 3536 | 0.2811 | 0.9053 | 0.9507 | 0.9275 | 0.9414 |
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+ | 0.1386 | 5.0 | 4420 | 0.3108 | 0.9019 | 0.9523 | 0.9265 | 0.9481 |
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+ | 0.1224 | 6.0 | 5304 | 0.3265 | 0.8978 | 0.9532 | 0.9247 | 0.9430 |
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+ | 0.0891 | 7.0 | 6188 | 0.3601 | 0.9071 | 0.9548 | 0.9303 | 0.9471 |
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+ | 0.08 | 8.0 | 7072 | 0.3555 | 0.8931 | 0.9540 | 0.9225 | 0.9458 |
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+ | 0.0547 | 9.0 | 7956 | 0.4065 | 0.9089 | 0.9589 | 0.9332 | 0.9482 |
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+ | 0.0539 | 10.0 | 8840 | 0.4109 | 0.9088 | 0.9581 | 0.9328 | 0.9478 |
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  ### Framework versions