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ner_based_bert-base-chinese

Token Classification
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bert
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  1. README.md +116 -118
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -1,118 +1,116 @@
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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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- language:
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- - zh
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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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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- 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
+ ---
15
+
16
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
+ should probably proofread and complete it, then remove this comment. -->
18
+
19
+ # ner_based_bert-base-chinese
20
+
21
+ 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.
22
+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0429
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+ - Precision: 0.9487
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+ - Recall: 0.9514
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+ - F1: 0.9501
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+ - Accuracy: 0.9957
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+
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+ ## Model description
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+
31
+ More information needed
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+
33
+ ## Intended uses & limitations
34
+
35
+ More information needed
36
+
37
+ ## Training and evaluation data
38
+
39
+ More information needed
40
+
41
+ ## Training procedure
42
+
43
+ ### Training hyperparameters
44
+
45
+ The following hyperparameters were used during training:
46
+ - learning_rate: 2e-05
47
+ - train_batch_size: 16
48
+ - eval_batch_size: 16
49
+ - 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
53
+ - 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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+ | 0.0294 | 1.0 | 1981 | 0.0255 | 0.8782 | 0.9345 | 0.9055 | 0.9923 |
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+ | 0.0169 | 2.0 | 3962 | 0.0214 | 0.9167 | 0.9362 | 0.9263 | 0.9942 |
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+ | 0.0113 | 3.0 | 5943 | 0.0213 | 0.9206 | 0.9464 | 0.9333 | 0.9948 |
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+ | 0.0069 | 4.0 | 7924 | 0.0228 | 0.9224 | 0.9518 | 0.9369 | 0.9949 |
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+ | 0.0044 | 5.0 | 9905 | 0.0228 | 0.9267 | 0.9432 | 0.9349 | 0.9950 |
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+ | 0.0039 | 6.0 | 11886 | 0.0255 | 0.9323 | 0.9416 | 0.9369 | 0.9949 |
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+ | 0.0024 | 7.0 | 13867 | 0.0300 | 0.9385 | 0.9443 | 0.9414 | 0.9951 |
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+ | 0.0028 | 8.0 | 15848 | 0.0276 | 0.9323 | 0.9491 | 0.9407 | 0.9952 |
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+ | 0.0022 | 9.0 | 17829 | 0.0297 | 0.9371 | 0.9459 | 0.9415 | 0.9952 |
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+ | 0.0019 | 10.0 | 19810 | 0.0306 | 0.9318 | 0.9479 | 0.9397 | 0.9950 |
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+ | 0.0012 | 11.0 | 21791 | 0.0326 | 0.9298 | 0.9500 | 0.9398 | 0.9951 |
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+ | 0.0016 | 12.0 | 23772 | 0.0344 | 0.9329 | 0.9496 | 0.9412 | 0.9951 |
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+ | 0.0011 | 13.0 | 25753 | 0.0355 | 0.9290 | 0.9511 | 0.9399 | 0.9951 |
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+ | 0.0009 | 14.0 | 27734 | 0.0307 | 0.9405 | 0.9461 | 0.9433 | 0.9954 |
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+ | 0.0009 | 15.0 | 29715 | 0.0336 | 0.9420 | 0.9430 | 0.9425 | 0.9953 |
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+ | 0.0006 | 16.0 | 31696 | 0.0351 | 0.9357 | 0.9446 | 0.9401 | 0.9950 |
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+ | 0.0007 | 17.0 | 33677 | 0.0326 | 0.9358 | 0.9484 | 0.9420 | 0.9952 |
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+ | 0.0008 | 18.0 | 35658 | 0.0345 | 0.9269 | 0.9549 | 0.9407 | 0.9951 |
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+ | 0.0007 | 19.0 | 37639 | 0.0347 | 0.9352 | 0.9523 | 0.9437 | 0.9953 |
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+ | 0.0005 | 20.0 | 39620 | 0.0344 | 0.9381 | 0.9509 | 0.9445 | 0.9956 |
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+ | 0.0006 | 21.0 | 41601 | 0.0360 | 0.9375 | 0.9498 | 0.9436 | 0.9953 |
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+ | 0.0004 | 22.0 | 43582 | 0.0365 | 0.9429 | 0.9473 | 0.9451 | 0.9954 |
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+ | 0.0005 | 23.0 | 45563 | 0.0362 | 0.9388 | 0.9495 | 0.9441 | 0.9952 |
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+ | 0.0004 | 24.0 | 47544 | 0.0384 | 0.9410 | 0.9444 | 0.9427 | 0.9951 |
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+ | 0.0003 | 25.0 | 49525 | 0.0361 | 0.9445 | 0.9479 | 0.9461 | 0.9956 |
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+ | 0.0003 | 26.0 | 51506 | 0.0372 | 0.9459 | 0.9466 | 0.9463 | 0.9955 |
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+ | 0.0004 | 27.0 | 53487 | 0.0390 | 0.9432 | 0.9461 | 0.9446 | 0.9954 |
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+ | 0.0003 | 28.0 | 55468 | 0.0381 | 0.9412 | 0.9466 | 0.9439 | 0.9955 |
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+ | 0.0004 | 29.0 | 57449 | 0.0404 | 0.9312 | 0.9540 | 0.9424 | 0.9952 |
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+ | 0.0002 | 30.0 | 59430 | 0.0397 | 0.9389 | 0.9509 | 0.9449 | 0.9955 |
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+ | 0.0003 | 31.0 | 61411 | 0.0388 | 0.9413 | 0.9488 | 0.9450 | 0.9954 |
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+ | 0.0002 | 32.0 | 63392 | 0.0409 | 0.9411 | 0.9473 | 0.9442 | 0.9953 |
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+ | 0.0003 | 33.0 | 65373 | 0.0418 | 0.9443 | 0.9509 | 0.9476 | 0.9955 |
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+ | 0.0002 | 34.0 | 67354 | 0.0413 | 0.9390 | 0.9529 | 0.9459 | 0.9955 |
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+ | 0.0001 | 35.0 | 69335 | 0.0418 | 0.9371 | 0.9516 | 0.9443 | 0.9953 |
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+ | 0.0001 | 36.0 | 71316 | 0.0427 | 0.9414 | 0.9525 | 0.9469 | 0.9955 |
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+ | 0.0001 | 37.0 | 73297 | 0.0439 | 0.9359 | 0.9538 | 0.9448 | 0.9953 |
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+ | 0.0001 | 38.0 | 75278 | 0.0418 | 0.9429 | 0.9509 | 0.9469 | 0.9955 |
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+ | 0.0 | 39.0 | 77259 | 0.0410 | 0.9511 | 0.9482 | 0.9497 | 0.9957 |
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+ | 0.0001 | 40.0 | 79240 | 0.0410 | 0.9473 | 0.9507 | 0.9490 | 0.9956 |
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+ | 0.0 | 41.0 | 81221 | 0.0442 | 0.9444 | 0.9532 | 0.9488 | 0.9956 |
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+ | 0.0001 | 42.0 | 83202 | 0.0419 | 0.9427 | 0.9522 | 0.9474 | 0.9956 |
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+ | 0.0001 | 43.0 | 85183 | 0.0423 | 0.9506 | 0.9477 | 0.9491 | 0.9956 |
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+ | 0.0001 | 44.0 | 87164 | 0.0426 | 0.9478 | 0.9498 | 0.9488 | 0.9956 |
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+ | 0.0001 | 45.0 | 89145 | 0.0426 | 0.9425 | 0.9523 | 0.9474 | 0.9956 |
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+ | 0.0 | 46.0 | 91126 | 0.0435 | 0.9433 | 0.9509 | 0.9471 | 0.9956 |
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+ | 0.0 | 47.0 | 93107 | 0.0437 | 0.9458 | 0.9500 | 0.9479 | 0.9956 |
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+ | 0.0 | 48.0 | 95088 | 0.0436 | 0.9458 | 0.9516 | 0.9487 | 0.9957 |
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+ | 0.0 | 49.0 | 97069 | 0.0428 | 0.9483 | 0.9507 | 0.9495 | 0.9957 |
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+ | 0.0 | 50.0 | 99050 | 0.0429 | 0.9487 | 0.9514 | 0.9501 | 0.9957 |
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+
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+
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+ ### Framework versions
112
+
113
+ - Transformers 4.48.3
114
+ - Pytorch 2.6.0+cu126
115
+ - Datasets 3.2.0
116
+ - Tokenizers 0.21.0
 
 
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