update model card README.md

README.md

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+---
+language:
+- zh
+tags:
+- generated_from_trainer
+datasets:
+- gyr66/privacy_detection
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-chinese-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: gyr66/privacy_detection
+      type: gyr66/privacy_detection
+      config: null
+      split: None
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.3840122394339262
+    - name: Recall
+      type: recall
+      value: 0.5055387713997986
+    - name: F1
+      type: f1
+      value: 0.43647429627214435
+    - name: Accuracy
+      type: accuracy
+      value: 0.8564247370217519
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bert-base-chinese-finetuned-ner
+
+This model is a fine-tuned version of [Danielwei0214/bert-base-chinese-finetuned-ner](https://huggingface.co/Danielwei0214/bert-base-chinese-finetuned-ner) on the gyr66/privacy_detection dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4895
+- Precision: 0.3840
+- Recall: 0.5055
+- F1: 0.4365
+- Accuracy: 0.8564
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 56
+- eval_batch_size: 56
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.3112        | 1.0   | 36   | 0.7305          | 0.2062    | 0.2535 | 0.2274 | 0.8028   |
+| 0.6083        | 2.0   | 72   | 0.5295          | 0.3598    | 0.4668 | 0.4064 | 0.8451   |
+| 0.4782        | 3.0   | 108  | 0.4895          | 0.3840    | 0.5055 | 0.4365 | 0.8564   |
+
+
+### Framework versions
+
+- Transformers 4.27.3
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.5
+- Tokenizers 0.13.2