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luohuashijieyoufengjun
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ner_based_modern_bert_multilingual

Token Classification
Transformers
Safetensors
Chinese
modernbert
Generated from Trainer
Model card Files
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1
- ---
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- library_name: transformers
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- license: apache-2.0
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- base_model: neavo/modern_bert_multilingual
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- tags:
6
- - generated_from_trainer
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- metrics:
8
- - precision
9
- - recall
10
- - f1
11
- - accuracy
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- model-index:
13
- - name: my_ner_model
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- results: []
15
- ---
16
-
17
- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
18
- should probably proofread and complete it, then remove this comment. -->
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-
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- # my_ner_model
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-
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- This model is a fine-tuned version of [neavo/modern_bert_multilingual](https://huggingface.co/neavo/modern_bert_multilingual) on the None dataset.
23
- It achieves the following results on the evaluation set:
24
- - Loss: 0.3580
25
- - Precision: 0.9041
26
- - Recall: 0.9232
27
- - F1: 0.9135
28
- - Accuracy: 0.9683
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-
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- ## Model description
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-
32
- More information needed
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-
34
- ## Intended uses & limitations
35
-
36
- More information needed
37
-
38
- ## Training and evaluation data
39
-
40
- More information needed
41
-
42
- ## Training procedure
43
-
44
- ### Training hyperparameters
45
-
46
- The following hyperparameters were used during training:
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- - learning_rate: 2e-05
48
- - train_batch_size: 16
49
- - eval_batch_size: 16
50
- - 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 | 0.2673 | 0.8862 | 0.9071 | 0.8965 | 0.9641 |
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- | 0.0058 | 2.0 | 734 | 0.2867 | 0.8698 | 0.9108 | 0.8898 | 0.9593 |
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- | 0.0058 | 3.0 | 1101 | 0.2704 | 0.8604 | 0.9009 | 0.8801 | 0.9635 |
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- | 0.0058 | 4.0 | 1468 | 0.2886 | 0.8985 | 0.9108 | 0.9046 | 0.9641 |
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- | 0.0039 | 5.0 | 1835 | 0.2870 | 0.9021 | 0.9133 | 0.9076 | 0.9642 |
65
- | 0.0018 | 6.0 | 2202 | 0.2958 | 0.8837 | 0.9133 | 0.8982 | 0.9641 |
66
- | 0.0009 | 7.0 | 2569 | 0.2912 | 0.8908 | 0.9095 | 0.9001 | 0.9651 |
67
- | 0.0009 | 8.0 | 2936 | 0.3250 | 0.8581 | 0.9145 | 0.8854 | 0.9595 |
68
- | 0.0017 | 9.0 | 3303 | 0.3341 | 0.8897 | 0.8897 | 0.8897 | 0.9614 |
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- | 0.0021 | 10.0 | 3670 | 0.2768 | 0.8772 | 0.9207 | 0.8984 | 0.9639 |
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- | 0.0015 | 11.0 | 4037 | 0.2907 | 0.8803 | 0.9207 | 0.9001 | 0.9665 |
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- | 0.0015 | 12.0 | 4404 | 0.3031 | 0.8961 | 0.9195 | 0.9076 | 0.9673 |
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- | 0.001 | 13.0 | 4771 | 0.2833 | 0.9061 | 0.9207 | 0.9133 | 0.9686 |
73
- | 0.0 | 14.0 | 5138 | 0.2925 | 0.9098 | 0.9244 | 0.9170 | 0.9693 |
74
- | 0.0 | 15.0 | 5505 | 0.2930 | 0.9068 | 0.9281 | 0.9173 | 0.9698 |
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- | 0.0 | 16.0 | 5872 | 0.2950 | 0.9060 | 0.9318 | 0.9188 | 0.9695 |
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- | 0.0 | 17.0 | 6239 | 0.2976 | 0.9037 | 0.9306 | 0.9170 | 0.9696 |
77
- | 0.0 | 18.0 | 6606 | 0.3001 | 0.9026 | 0.9306 | 0.9164 | 0.9692 |
78
- | 0.0 | 19.0 | 6973 | 0.3025 | 0.9025 | 0.9294 | 0.9158 | 0.9692 |
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- | 0.0 | 20.0 | 7340 | 0.3050 | 0.9025 | 0.9294 | 0.9158 | 0.9688 |
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- | 0.0 | 21.0 | 7707 | 0.3075 | 0.9025 | 0.9294 | 0.9158 | 0.9688 |
81
- | 0.0 | 22.0 | 8074 | 0.3096 | 0.9048 | 0.9306 | 0.9175 | 0.9690 |
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- | 0.0 | 23.0 | 8441 | 0.3120 | 0.9070 | 0.9306 | 0.9187 | 0.9688 |
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- | 0.0 | 24.0 | 8808 | 0.3141 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
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- | 0.0 | 25.0 | 9175 | 0.3165 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
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- | 0.0 | 26.0 | 9542 | 0.3188 | 0.9013 | 0.9281 | 0.9145 | 0.9686 |
86
- | 0.0 | 27.0 | 9909 | 0.3210 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
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- | 0.0 | 28.0 | 10276 | 0.3232 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
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- | 0.0 | 29.0 | 10643 | 0.3256 | 0.9057 | 0.9281 | 0.9168 | 0.9686 |
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- | 0.0 | 30.0 | 11010 | 0.3278 | 0.9057 | 0.9281 | 0.9168 | 0.9686 |
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- | 0.0 | 31.0 | 11377 | 0.3299 | 0.9045 | 0.9269 | 0.9155 | 0.9686 |
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- | 0.0 | 32.0 | 11744 | 0.3322 | 0.9045 | 0.9269 | 0.9155 | 0.9685 |
92
- | 0.0 | 33.0 | 12111 | 0.3340 | 0.9045 | 0.9269 | 0.9155 | 0.9685 |
93
- | 0.0 | 34.0 | 12478 | 0.3363 | 0.9045 | 0.9269 | 0.9155 | 0.9685 |
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- | 0.0 | 35.0 | 12845 | 0.3384 | 0.9056 | 0.9269 | 0.9161 | 0.9685 |
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- | 0.0 | 36.0 | 13212 | 0.3406 | 0.9044 | 0.9257 | 0.9149 | 0.9685 |
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- | 0.0 | 37.0 | 13579 | 0.3424 | 0.9044 | 0.9257 | 0.9149 | 0.9685 |
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- | 0.0 | 38.0 | 13946 | 0.3442 | 0.9030 | 0.9232 | 0.9130 | 0.9683 |
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- | 0.0 | 39.0 | 14313 | 0.3461 | 0.9031 | 0.9244 | 0.9137 | 0.9686 |
99
- | 0.0 | 40.0 | 14680 | 0.3479 | 0.9030 | 0.9232 | 0.9130 | 0.9686 |
100
- | 0.0 | 41.0 | 15047 | 0.3495 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
101
- | 0.0 | 42.0 | 15414 | 0.3510 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
102
- | 0.0 | 43.0 | 15781 | 0.3525 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
103
- | 0.0 | 44.0 | 16148 | 0.3539 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
104
- | 0.0 | 45.0 | 16515 | 0.3551 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
105
- | 0.0 | 46.0 | 16882 | 0.3562 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
106
- | 0.0 | 47.0 | 17249 | 0.3570 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
107
- | 0.0 | 48.0 | 17616 | 0.3575 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
108
- | 0.0 | 49.0 | 17983 | 0.3580 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
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- | 0.0 | 50.0 | 18350 | 0.3580 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
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-
111
-
112
- ### Framework versions
113
-
114
- - Transformers 4.48.3
115
- - Pytorch 2.6.0+cu126
116
- - Datasets 3.2.0
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- - Tokenizers 0.21.0
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ license: apache-2.0
4
+ base_model: neavo/modern_bert_multilingual
5
+ tags:
6
+ - generated_from_trainer
7
+ metrics:
8
+ - precision
9
+ - recall
10
+ - f1
11
+ - accuracy
12
+ model-index:
13
+ - name: my_ner_model
14
+ results: []
15
+ language:
16
+ - zh
17
+ ---
18
+
19
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
20
+ should probably proofread and complete it, then remove this comment. -->
21
+
22
+ # my_ner_model
23
+
24
+ This model is a fine-tuned version of [neavo/modern_bert_multilingual](https://huggingface.co/neavo/modern_bert_multilingual) on the None dataset.
25
+ It achieves the following results on the evaluation set:
26
+ - Loss: 0.3580
27
+ - Precision: 0.9041
28
+ - Recall: 0.9232
29
+ - F1: 0.9135
30
+ - Accuracy: 0.9683
31
+
32
+ ## Model description
33
+
34
+ More information needed
35
+
36
+ ## Intended uses & limitations
37
+
38
+ More information needed
39
+
40
+ ## Training and evaluation data
41
+
42
+ More information needed
43
+
44
+ ## Training procedure
45
+
46
+ ### Training hyperparameters
47
+
48
+ The following hyperparameters were used during training:
49
+ - learning_rate: 2e-05
50
+ - train_batch_size: 16
51
+ - eval_batch_size: 16
52
+ - seed: 42
53
+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
54
+ - lr_scheduler_type: linear
55
+ - num_epochs: 50
56
+ - mixed_precision_training: Native AMP
57
+
58
+ ### Training results
59
+
60
+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
61
+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
62
+ | No log | 1.0 | 367 | 0.2673 | 0.8862 | 0.9071 | 0.8965 | 0.9641 |
63
+ | 0.0058 | 2.0 | 734 | 0.2867 | 0.8698 | 0.9108 | 0.8898 | 0.9593 |
64
+ | 0.0058 | 3.0 | 1101 | 0.2704 | 0.8604 | 0.9009 | 0.8801 | 0.9635 |
65
+ | 0.0058 | 4.0 | 1468 | 0.2886 | 0.8985 | 0.9108 | 0.9046 | 0.9641 |
66
+ | 0.0039 | 5.0 | 1835 | 0.2870 | 0.9021 | 0.9133 | 0.9076 | 0.9642 |
67
+ | 0.0018 | 6.0 | 2202 | 0.2958 | 0.8837 | 0.9133 | 0.8982 | 0.9641 |
68
+ | 0.0009 | 7.0 | 2569 | 0.2912 | 0.8908 | 0.9095 | 0.9001 | 0.9651 |
69
+ | 0.0009 | 8.0 | 2936 | 0.3250 | 0.8581 | 0.9145 | 0.8854 | 0.9595 |
70
+ | 0.0017 | 9.0 | 3303 | 0.3341 | 0.8897 | 0.8897 | 0.8897 | 0.9614 |
71
+ | 0.0021 | 10.0 | 3670 | 0.2768 | 0.8772 | 0.9207 | 0.8984 | 0.9639 |
72
+ | 0.0015 | 11.0 | 4037 | 0.2907 | 0.8803 | 0.9207 | 0.9001 | 0.9665 |
73
+ | 0.0015 | 12.0 | 4404 | 0.3031 | 0.8961 | 0.9195 | 0.9076 | 0.9673 |
74
+ | 0.001 | 13.0 | 4771 | 0.2833 | 0.9061 | 0.9207 | 0.9133 | 0.9686 |
75
+ | 0.0 | 14.0 | 5138 | 0.2925 | 0.9098 | 0.9244 | 0.9170 | 0.9693 |
76
+ | 0.0 | 15.0 | 5505 | 0.2930 | 0.9068 | 0.9281 | 0.9173 | 0.9698 |
77
+ | 0.0 | 16.0 | 5872 | 0.2950 | 0.9060 | 0.9318 | 0.9188 | 0.9695 |
78
+ | 0.0 | 17.0 | 6239 | 0.2976 | 0.9037 | 0.9306 | 0.9170 | 0.9696 |
79
+ | 0.0 | 18.0 | 6606 | 0.3001 | 0.9026 | 0.9306 | 0.9164 | 0.9692 |
80
+ | 0.0 | 19.0 | 6973 | 0.3025 | 0.9025 | 0.9294 | 0.9158 | 0.9692 |
81
+ | 0.0 | 20.0 | 7340 | 0.3050 | 0.9025 | 0.9294 | 0.9158 | 0.9688 |
82
+ | 0.0 | 21.0 | 7707 | 0.3075 | 0.9025 | 0.9294 | 0.9158 | 0.9688 |
83
+ | 0.0 | 22.0 | 8074 | 0.3096 | 0.9048 | 0.9306 | 0.9175 | 0.9690 |
84
+ | 0.0 | 23.0 | 8441 | 0.3120 | 0.9070 | 0.9306 | 0.9187 | 0.9688 |
85
+ | 0.0 | 24.0 | 8808 | 0.3141 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
86
+ | 0.0 | 25.0 | 9175 | 0.3165 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
87
+ | 0.0 | 26.0 | 9542 | 0.3188 | 0.9013 | 0.9281 | 0.9145 | 0.9686 |
88
+ | 0.0 | 27.0 | 9909 | 0.3210 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
89
+ | 0.0 | 28.0 | 10276 | 0.3232 | 0.9024 | 0.9281 | 0.9151 | 0.9685 |
90
+ | 0.0 | 29.0 | 10643 | 0.3256 | 0.9057 | 0.9281 | 0.9168 | 0.9686 |
91
+ | 0.0 | 30.0 | 11010 | 0.3278 | 0.9057 | 0.9281 | 0.9168 | 0.9686 |
92
+ | 0.0 | 31.0 | 11377 | 0.3299 | 0.9045 | 0.9269 | 0.9155 | 0.9686 |
93
+ | 0.0 | 32.0 | 11744 | 0.3322 | 0.9045 | 0.9269 | 0.9155 | 0.9685 |
94
+ | 0.0 | 33.0 | 12111 | 0.3340 | 0.9045 | 0.9269 | 0.9155 | 0.9685 |
95
+ | 0.0 | 34.0 | 12478 | 0.3363 | 0.9045 | 0.9269 | 0.9155 | 0.9685 |
96
+ | 0.0 | 35.0 | 12845 | 0.3384 | 0.9056 | 0.9269 | 0.9161 | 0.9685 |
97
+ | 0.0 | 36.0 | 13212 | 0.3406 | 0.9044 | 0.9257 | 0.9149 | 0.9685 |
98
+ | 0.0 | 37.0 | 13579 | 0.3424 | 0.9044 | 0.9257 | 0.9149 | 0.9685 |
99
+ | 0.0 | 38.0 | 13946 | 0.3442 | 0.9030 | 0.9232 | 0.9130 | 0.9683 |
100
+ | 0.0 | 39.0 | 14313 | 0.3461 | 0.9031 | 0.9244 | 0.9137 | 0.9686 |
101
+ | 0.0 | 40.0 | 14680 | 0.3479 | 0.9030 | 0.9232 | 0.9130 | 0.9686 |
102
+ | 0.0 | 41.0 | 15047 | 0.3495 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
103
+ | 0.0 | 42.0 | 15414 | 0.3510 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
104
+ | 0.0 | 43.0 | 15781 | 0.3525 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
105
+ | 0.0 | 44.0 | 16148 | 0.3539 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
106
+ | 0.0 | 45.0 | 16515 | 0.3551 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
107
+ | 0.0 | 46.0 | 16882 | 0.3562 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
108
+ | 0.0 | 47.0 | 17249 | 0.3570 | 0.9030 | 0.9232 | 0.9130 | 0.9685 |
109
+ | 0.0 | 48.0 | 17616 | 0.3575 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
110
+ | 0.0 | 49.0 | 17983 | 0.3580 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
111
+ | 0.0 | 50.0 | 18350 | 0.3580 | 0.9041 | 0.9232 | 0.9135 | 0.9683 |
112
+
113
+
114
+ ### Framework versions
115
+
116
+ - Transformers 4.48.3
117
+ - Pytorch 2.6.0+cu126
118
+ - Datasets 3.2.0
119
+ - Tokenizers 0.21.0