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bert-base-chinese-finetuned-ner

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update model card README.md

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@@ -21,16 +21,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.0
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  - name: Recall
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  type: recall
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- value: 0.0
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  - name: F1
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  type: f1
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- value: 0.0
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  - name: Accuracy
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  type: accuracy
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- value: 0.6147582697201018
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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
@@ -40,11 +40,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7493
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- - Precision: 0.0
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- - Recall: 0.0
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- - F1: 0.0
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- - Accuracy: 0.6148
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  ## Model description
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@@ -64,20 +64,47 @@ More information needed
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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: 8
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- - eval_batch_size: 8
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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: 3
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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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- | No log | 1.0 | 3 | 2.1820 | 0.0 | 0.0 | 0.0 | 0.6125 |
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- | No log | 2.0 | 6 | 1.8292 | 0.0 | 0.0 | 0.0 | 0.6148 |
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- | No log | 3.0 | 9 | 1.7493 | 0.0 | 0.0 | 0.0 | 0.6148 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9146341463414634
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  - name: Recall
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  type: recall
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+ value: 0.9414225941422594
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  - name: F1
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  type: f1
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+ value: 0.9278350515463917
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9750636132315522
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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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  This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1016
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+ - Precision: 0.9146
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+ - Recall: 0.9414
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+ - F1: 0.9278
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+ - Accuracy: 0.9751
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  ## Model description
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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: 10
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+ - eval_batch_size: 10
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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: 30
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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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+ | No log | 1.0 | 2 | 0.9181 | 0.1271 | 0.1255 | 0.1263 | 0.7170 |
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+ | No log | 2.0 | 4 | 0.8048 | 0.1919 | 0.2385 | 0.2127 | 0.7669 |
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+ | No log | 3.0 | 6 | 0.7079 | 0.2422 | 0.3264 | 0.2781 | 0.7980 |
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+ | No log | 4.0 | 8 | 0.6201 | 0.3505 | 0.4854 | 0.4070 | 0.8338 |
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+ | No log | 5.0 | 10 | 0.5462 | 0.3898 | 0.4812 | 0.4307 | 0.8611 |
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+ | No log | 6.0 | 12 | 0.4851 | 0.4749 | 0.5941 | 0.5279 | 0.8802 |
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+ | No log | 7.0 | 14 | 0.4338 | 0.5213 | 0.6151 | 0.5643 | 0.8936 |
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+ | No log | 8.0 | 16 | 0.3843 | 0.5663 | 0.6611 | 0.6100 | 0.9076 |
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+ | No log | 9.0 | 18 | 0.3451 | 0.6255 | 0.6987 | 0.6601 | 0.9214 |
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+ | No log | 10.0 | 20 | 0.3058 | 0.6719 | 0.7197 | 0.6949 | 0.9293 |
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+ | No log | 11.0 | 22 | 0.2783 | 0.6808 | 0.7406 | 0.7094 | 0.9344 |
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+ | No log | 12.0 | 24 | 0.2497 | 0.7050 | 0.7699 | 0.7360 | 0.9427 |
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+ | No log | 13.0 | 26 | 0.2235 | 0.7519 | 0.8117 | 0.7807 | 0.9506 |
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+ | No log | 14.0 | 28 | 0.2031 | 0.7713 | 0.8326 | 0.8008 | 0.9552 |
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+ | No log | 15.0 | 30 | 0.1861 | 0.7915 | 0.8577 | 0.8233 | 0.9593 |
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+ | No log | 16.0 | 32 | 0.1726 | 0.8031 | 0.8703 | 0.8353 | 0.9613 |
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+ | No log | 17.0 | 34 | 0.1619 | 0.8320 | 0.8912 | 0.8606 | 0.9641 |
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+ | No log | 18.0 | 36 | 0.1521 | 0.8571 | 0.9038 | 0.8798 | 0.9674 |
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+ | No log | 19.0 | 38 | 0.1420 | 0.8710 | 0.9038 | 0.8871 | 0.9695 |
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+ | No log | 20.0 | 40 | 0.1352 | 0.8795 | 0.9163 | 0.8975 | 0.9700 |
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+ | No log | 21.0 | 42 | 0.1281 | 0.8755 | 0.9121 | 0.8934 | 0.9712 |
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+ | No log | 22.0 | 44 | 0.1209 | 0.8916 | 0.9289 | 0.9098 | 0.9728 |
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+ | No log | 23.0 | 46 | 0.1155 | 0.8924 | 0.9372 | 0.9143 | 0.9733 |
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+ | No log | 24.0 | 48 | 0.1115 | 0.904 | 0.9456 | 0.9243 | 0.9746 |
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+ | No log | 25.0 | 50 | 0.1087 | 0.9116 | 0.9498 | 0.9303 | 0.9746 |
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+ | No log | 26.0 | 52 | 0.1068 | 0.9146 | 0.9414 | 0.9278 | 0.9740 |
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+ | No log | 27.0 | 54 | 0.1054 | 0.9146 | 0.9414 | 0.9278 | 0.9743 |
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+ | No log | 28.0 | 56 | 0.1036 | 0.9146 | 0.9414 | 0.9278 | 0.9743 |
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+ | No log | 29.0 | 58 | 0.1022 | 0.9146 | 0.9414 | 0.9278 | 0.9746 |
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+ | No log | 30.0 | 60 | 0.1016 | 0.9146 | 0.9414 | 0.9278 | 0.9751 |
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  ### Framework versions