update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-chinese-wikiann-zh-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      config: zh
+      split: validation
+      args: zh
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7890612756621219
+    - name: Recall
+      type: recall
+      value: 0.8060513887777155
+    - name: F1
+      type: f1
+      value: 0.797465848346862
+    - name: Accuracy
+      type: accuracy
+      value: 0.9432393178410795
+---
+
+<!-- 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-wikiann-zh-ner
+
+This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2092
+- Precision: 0.7891
+- Recall: 0.8061
+- F1: 0.7975
+- Accuracy: 0.9432
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.842         | 0.16  | 400  | 0.3530          | 0.5535    | 0.6872 | 0.6131 | 0.8927   |
+| 0.32          | 0.32  | 800  | 0.2800          | 0.6929    | 0.6749 | 0.6838 | 0.9190   |
+| 0.2928        | 0.48  | 1200 | 0.2438          | 0.7031    | 0.7661 | 0.7333 | 0.9301   |
+| 0.245         | 0.64  | 1600 | 0.2525          | 0.6959    | 0.7919 | 0.7408 | 0.9280   |
+| 0.2236        | 0.8   | 2000 | 0.2315          | 0.7441    | 0.7503 | 0.7472 | 0.9342   |
+| 0.2444        | 0.96  | 2400 | 0.2119          | 0.7719    | 0.7675 | 0.7697 | 0.9379   |
+| 0.1899        | 1.12  | 2800 | 0.2267          | 0.7531    | 0.8062 | 0.7788 | 0.9387   |
+| 0.1649        | 1.28  | 3200 | 0.2249          | 0.7519    | 0.8202 | 0.7846 | 0.9395   |
+| 0.1521        | 1.44  | 3600 | 0.2220          | 0.7778    | 0.8032 | 0.7903 | 0.9413   |
+| 0.1787        | 1.6   | 4000 | 0.2185          | 0.7879    | 0.7860 | 0.7869 | 0.9417   |
+| 0.146         | 1.76  | 4400 | 0.2134          | 0.7721    | 0.8128 | 0.7919 | 0.9416   |
+| 0.1557        | 1.92  | 4800 | 0.2111          | 0.7857    | 0.8101 | 0.7977 | 0.9429   |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3