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ner_based_bert-base-chinese

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bert
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- ---
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- library_name: transformers
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- base_model: google-bert/bert-base-chinese
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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: ner_based_bert-base-chinese
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- results: []
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- ---
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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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-
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- # ner_based_bert-base-chinese
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-
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- This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the None dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.1461
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- - Precision: 0.9651
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- - Recall: 0.9712
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- - F1: 0.9681
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- - Accuracy: 0.9873
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
35
- More information needed
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-
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- ## Training and evaluation data
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-
39
- More information needed
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-
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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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: 16
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- - eval_batch_size: 16
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- - seed: 42
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- - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- - lr_scheduler_type: linear
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- - num_epochs: 50
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- - mixed_precision_training: Native AMP
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-
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- ### Training results
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-
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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 | 367 | 0.1262 | 0.9355 | 0.9449 | 0.9401 | 0.9738 |
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- | 0.0701 | 2.0 | 734 | 0.0725 | 0.9663 | 0.9687 | 0.9675 | 0.9867 |
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- | 0.0494 | 3.0 | 1101 | 0.0769 | 0.9663 | 0.9712 | 0.9688 | 0.9871 |
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- | 0.0494 | 4.0 | 1468 | 0.0902 | 0.9653 | 0.9749 | 0.9701 | 0.9880 |
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- | 0.0306 | 5.0 | 1835 | 0.0796 | 0.9665 | 0.9749 | 0.9707 | 0.9876 |
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- | 0.0245 | 6.0 | 2202 | 0.0968 | 0.9509 | 0.9699 | 0.9603 | 0.9847 |
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- | 0.0221 | 7.0 | 2569 | 0.0956 | 0.9638 | 0.9674 | 0.9656 | 0.9864 |
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- | 0.0221 | 8.0 | 2936 | 0.0983 | 0.9698 | 0.9662 | 0.9680 | 0.9877 |
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- | 0.0154 | 9.0 | 3303 | 0.0958 | 0.9589 | 0.9649 | 0.9619 | 0.9867 |
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- | 0.0145 | 10.0 | 3670 | 0.1168 | 0.9614 | 0.9674 | 0.9644 | 0.9861 |
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- | 0.0104 | 11.0 | 4037 | 0.1010 | 0.9653 | 0.9762 | 0.9707 | 0.9883 |
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- | 0.0104 | 12.0 | 4404 | 0.1306 | 0.9554 | 0.9674 | 0.9614 | 0.9841 |
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- | 0.0115 | 13.0 | 4771 | 0.1135 | 0.9540 | 0.9612 | 0.9576 | 0.9855 |
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- | 0.0099 | 14.0 | 5138 | 0.0968 | 0.9675 | 0.9699 | 0.9687 | 0.9889 |
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- | 0.0066 | 15.0 | 5505 | 0.1148 | 0.9636 | 0.9624 | 0.9630 | 0.9864 |
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- | 0.0066 | 16.0 | 5872 | 0.0903 | 0.9650 | 0.9687 | 0.9669 | 0.9894 |
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- | 0.0049 | 17.0 | 6239 | 0.1217 | 0.9649 | 0.9649 | 0.9649 | 0.9853 |
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- | 0.0049 | 18.0 | 6606 | 0.1147 | 0.9626 | 0.9674 | 0.965 | 0.9865 |
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- | 0.0049 | 19.0 | 6973 | 0.1154 | 0.9675 | 0.9712 | 0.9694 | 0.9874 |
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- | 0.0022 | 20.0 | 7340 | 0.1007 | 0.9676 | 0.9737 | 0.9706 | 0.9885 |
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- | 0.0024 | 21.0 | 7707 | 0.1255 | 0.9687 | 0.9699 | 0.9693 | 0.9877 |
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- | 0.0015 | 22.0 | 8074 | 0.1439 | 0.9651 | 0.9699 | 0.9675 | 0.9853 |
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- | 0.0015 | 23.0 | 8441 | 0.1346 | 0.9688 | 0.9724 | 0.9706 | 0.9873 |
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- | 0.003 | 24.0 | 8808 | 0.1243 | 0.9676 | 0.9724 | 0.97 | 0.9868 |
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- | 0.0016 | 25.0 | 9175 | 0.1278 | 0.9640 | 0.9737 | 0.9688 | 0.9874 |
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- | 0.0025 | 26.0 | 9542 | 0.1216 | 0.9593 | 0.9737 | 0.9664 | 0.9880 |
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- | 0.0025 | 27.0 | 9909 | 0.1290 | 0.9652 | 0.9737 | 0.9694 | 0.9880 |
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- | 0.0007 | 28.0 | 10276 | 0.1389 | 0.9613 | 0.9662 | 0.9637 | 0.9861 |
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- | 0.0013 | 29.0 | 10643 | 0.1306 | 0.9637 | 0.9662 | 0.9650 | 0.9867 |
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- | 0.0015 | 30.0 | 11010 | 0.1452 | 0.9613 | 0.9662 | 0.9637 | 0.9867 |
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- | 0.0015 | 31.0 | 11377 | 0.1405 | 0.9673 | 0.9649 | 0.9661 | 0.9861 |
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- | 0.0014 | 32.0 | 11744 | 0.1428 | 0.9626 | 0.9674 | 0.965 | 0.9870 |
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- | 0.0002 | 33.0 | 12111 | 0.1530 | 0.9650 | 0.9662 | 0.9656 | 0.9867 |
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- | 0.0002 | 34.0 | 12478 | 0.1525 | 0.9699 | 0.9687 | 0.9693 | 0.9867 |
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- | 0.0006 | 35.0 | 12845 | 0.1372 | 0.9688 | 0.9712 | 0.9700 | 0.9874 |
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- | 0.0004 | 36.0 | 13212 | 0.1359 | 0.9689 | 0.9762 | 0.9725 | 0.9885 |
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- | 0.0005 | 37.0 | 13579 | 0.1432 | 0.9688 | 0.9737 | 0.9713 | 0.9879 |
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- | 0.0005 | 38.0 | 13946 | 0.1443 | 0.9676 | 0.9724 | 0.97 | 0.9876 |
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- | 0.0006 | 39.0 | 14313 | 0.1414 | 0.9688 | 0.9724 | 0.9706 | 0.9880 |
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- | 0.0005 | 40.0 | 14680 | 0.1511 | 0.9663 | 0.9687 | 0.9675 | 0.9871 |
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- | 0.0003 | 41.0 | 15047 | 0.1438 | 0.9639 | 0.9712 | 0.9675 | 0.9873 |
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- | 0.0003 | 42.0 | 15414 | 0.1519 | 0.9650 | 0.9687 | 0.9669 | 0.9873 |
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- | 0.0001 | 43.0 | 15781 | 0.1580 | 0.9638 | 0.9687 | 0.9662 | 0.9867 |
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- | 0.0004 | 44.0 | 16148 | 0.1462 | 0.9650 | 0.9687 | 0.9669 | 0.9868 |
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- | 0.0001 | 45.0 | 16515 | 0.1478 | 0.9651 | 0.9699 | 0.9675 | 0.9868 |
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- | 0.0001 | 46.0 | 16882 | 0.1461 | 0.9663 | 0.9712 | 0.9688 | 0.9870 |
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- | 0.0002 | 47.0 | 17249 | 0.1456 | 0.9663 | 0.9712 | 0.9688 | 0.9870 |
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- | 0.0001 | 48.0 | 17616 | 0.1451 | 0.9651 | 0.9712 | 0.9681 | 0.9871 |
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- | 0.0001 | 49.0 | 17983 | 0.1456 | 0.9651 | 0.9712 | 0.9681 | 0.9871 |
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- | 0.0001 | 50.0 | 18350 | 0.1461 | 0.9651 | 0.9712 | 0.9681 | 0.9873 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.48.3
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- - Pytorch 2.6.0+cu126
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- - Datasets 3.2.0
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- - Tokenizers 0.21.0
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ base_model: google-bert/bert-base-chinese
4
+ tags:
5
+ - generated_from_trainer
6
+ metrics:
7
+ - precision
8
+ - recall
9
+ - f1
10
+ - accuracy
11
+ model-index:
12
+ - name: ner_based_bert-base-chinese
13
+ results: []
14
+ language:
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+ - zh
16
+ ---
17
+
18
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
19
+ should probably proofread and complete it, then remove this comment. -->
20
+
21
+ # ner_based_bert-base-chinese
22
+
23
+ This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the None dataset.
24
+ It achieves the following results on the evaluation set:
25
+ - Loss: 0.1461
26
+ - Precision: 0.9651
27
+ - Recall: 0.9712
28
+ - F1: 0.9681
29
+ - Accuracy: 0.9873
30
+
31
+ ## Model description
32
+
33
+ More information needed
34
+
35
+ ## Intended uses & limitations
36
+
37
+ More information needed
38
+
39
+ ## Training and evaluation data
40
+
41
+ More information needed
42
+
43
+ ## Training procedure
44
+
45
+ ### Training hyperparameters
46
+
47
+ The following hyperparameters were used during training:
48
+ - learning_rate: 2e-05
49
+ - train_batch_size: 16
50
+ - eval_batch_size: 16
51
+ - seed: 42
52
+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
53
+ - lr_scheduler_type: linear
54
+ - num_epochs: 50
55
+ - mixed_precision_training: Native AMP
56
+
57
+ ### Training results
58
+
59
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
60
+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 367 | 0.1262 | 0.9355 | 0.9449 | 0.9401 | 0.9738 |
62
+ | 0.0701 | 2.0 | 734 | 0.0725 | 0.9663 | 0.9687 | 0.9675 | 0.9867 |
63
+ | 0.0494 | 3.0 | 1101 | 0.0769 | 0.9663 | 0.9712 | 0.9688 | 0.9871 |
64
+ | 0.0494 | 4.0 | 1468 | 0.0902 | 0.9653 | 0.9749 | 0.9701 | 0.9880 |
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+ | 0.0306 | 5.0 | 1835 | 0.0796 | 0.9665 | 0.9749 | 0.9707 | 0.9876 |
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+ | 0.0245 | 6.0 | 2202 | 0.0968 | 0.9509 | 0.9699 | 0.9603 | 0.9847 |
67
+ | 0.0221 | 7.0 | 2569 | 0.0956 | 0.9638 | 0.9674 | 0.9656 | 0.9864 |
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+ | 0.0221 | 8.0 | 2936 | 0.0983 | 0.9698 | 0.9662 | 0.9680 | 0.9877 |
69
+ | 0.0154 | 9.0 | 3303 | 0.0958 | 0.9589 | 0.9649 | 0.9619 | 0.9867 |
70
+ | 0.0145 | 10.0 | 3670 | 0.1168 | 0.9614 | 0.9674 | 0.9644 | 0.9861 |
71
+ | 0.0104 | 11.0 | 4037 | 0.1010 | 0.9653 | 0.9762 | 0.9707 | 0.9883 |
72
+ | 0.0104 | 12.0 | 4404 | 0.1306 | 0.9554 | 0.9674 | 0.9614 | 0.9841 |
73
+ | 0.0115 | 13.0 | 4771 | 0.1135 | 0.9540 | 0.9612 | 0.9576 | 0.9855 |
74
+ | 0.0099 | 14.0 | 5138 | 0.0968 | 0.9675 | 0.9699 | 0.9687 | 0.9889 |
75
+ | 0.0066 | 15.0 | 5505 | 0.1148 | 0.9636 | 0.9624 | 0.9630 | 0.9864 |
76
+ | 0.0066 | 16.0 | 5872 | 0.0903 | 0.9650 | 0.9687 | 0.9669 | 0.9894 |
77
+ | 0.0049 | 17.0 | 6239 | 0.1217 | 0.9649 | 0.9649 | 0.9649 | 0.9853 |
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+ | 0.0049 | 18.0 | 6606 | 0.1147 | 0.9626 | 0.9674 | 0.965 | 0.9865 |
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+ | 0.0049 | 19.0 | 6973 | 0.1154 | 0.9675 | 0.9712 | 0.9694 | 0.9874 |
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+ | 0.0022 | 20.0 | 7340 | 0.1007 | 0.9676 | 0.9737 | 0.9706 | 0.9885 |
81
+ | 0.0024 | 21.0 | 7707 | 0.1255 | 0.9687 | 0.9699 | 0.9693 | 0.9877 |
82
+ | 0.0015 | 22.0 | 8074 | 0.1439 | 0.9651 | 0.9699 | 0.9675 | 0.9853 |
83
+ | 0.0015 | 23.0 | 8441 | 0.1346 | 0.9688 | 0.9724 | 0.9706 | 0.9873 |
84
+ | 0.003 | 24.0 | 8808 | 0.1243 | 0.9676 | 0.9724 | 0.97 | 0.9868 |
85
+ | 0.0016 | 25.0 | 9175 | 0.1278 | 0.9640 | 0.9737 | 0.9688 | 0.9874 |
86
+ | 0.0025 | 26.0 | 9542 | 0.1216 | 0.9593 | 0.9737 | 0.9664 | 0.9880 |
87
+ | 0.0025 | 27.0 | 9909 | 0.1290 | 0.9652 | 0.9737 | 0.9694 | 0.9880 |
88
+ | 0.0007 | 28.0 | 10276 | 0.1389 | 0.9613 | 0.9662 | 0.9637 | 0.9861 |
89
+ | 0.0013 | 29.0 | 10643 | 0.1306 | 0.9637 | 0.9662 | 0.9650 | 0.9867 |
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+ | 0.0015 | 30.0 | 11010 | 0.1452 | 0.9613 | 0.9662 | 0.9637 | 0.9867 |
91
+ | 0.0015 | 31.0 | 11377 | 0.1405 | 0.9673 | 0.9649 | 0.9661 | 0.9861 |
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+ | 0.0014 | 32.0 | 11744 | 0.1428 | 0.9626 | 0.9674 | 0.965 | 0.9870 |
93
+ | 0.0002 | 33.0 | 12111 | 0.1530 | 0.9650 | 0.9662 | 0.9656 | 0.9867 |
94
+ | 0.0002 | 34.0 | 12478 | 0.1525 | 0.9699 | 0.9687 | 0.9693 | 0.9867 |
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+ | 0.0006 | 35.0 | 12845 | 0.1372 | 0.9688 | 0.9712 | 0.9700 | 0.9874 |
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+ | 0.0004 | 36.0 | 13212 | 0.1359 | 0.9689 | 0.9762 | 0.9725 | 0.9885 |
97
+ | 0.0005 | 37.0 | 13579 | 0.1432 | 0.9688 | 0.9737 | 0.9713 | 0.9879 |
98
+ | 0.0005 | 38.0 | 13946 | 0.1443 | 0.9676 | 0.9724 | 0.97 | 0.9876 |
99
+ | 0.0006 | 39.0 | 14313 | 0.1414 | 0.9688 | 0.9724 | 0.9706 | 0.9880 |
100
+ | 0.0005 | 40.0 | 14680 | 0.1511 | 0.9663 | 0.9687 | 0.9675 | 0.9871 |
101
+ | 0.0003 | 41.0 | 15047 | 0.1438 | 0.9639 | 0.9712 | 0.9675 | 0.9873 |
102
+ | 0.0003 | 42.0 | 15414 | 0.1519 | 0.9650 | 0.9687 | 0.9669 | 0.9873 |
103
+ | 0.0001 | 43.0 | 15781 | 0.1580 | 0.9638 | 0.9687 | 0.9662 | 0.9867 |
104
+ | 0.0004 | 44.0 | 16148 | 0.1462 | 0.9650 | 0.9687 | 0.9669 | 0.9868 |
105
+ | 0.0001 | 45.0 | 16515 | 0.1478 | 0.9651 | 0.9699 | 0.9675 | 0.9868 |
106
+ | 0.0001 | 46.0 | 16882 | 0.1461 | 0.9663 | 0.9712 | 0.9688 | 0.9870 |
107
+ | 0.0002 | 47.0 | 17249 | 0.1456 | 0.9663 | 0.9712 | 0.9688 | 0.9870 |
108
+ | 0.0001 | 48.0 | 17616 | 0.1451 | 0.9651 | 0.9712 | 0.9681 | 0.9871 |
109
+ | 0.0001 | 49.0 | 17983 | 0.1456 | 0.9651 | 0.9712 | 0.9681 | 0.9871 |
110
+ | 0.0001 | 50.0 | 18350 | 0.1461 | 0.9651 | 0.9712 | 0.9681 | 0.9873 |
111
+
112
+
113
+ ### Framework versions
114
+
115
+ - Transformers 4.48.3
116
+ - Pytorch 2.6.0+cu126
117
+ - Datasets 3.2.0
118
+ - Tokenizers 0.21.0