---
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