---
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_based_bert-base-chinese
  results: []
---

<!-- 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. -->

# ner_based_bert-base-chinese

This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0171
- Precision: 0.9610
- Recall: 0.9716
- F1: 0.9663
- Accuracy: 0.9973

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0941        | 1.0   | 650  | 0.0194          | 0.9150    | 0.9327 | 0.9237 | 0.9943   |
| 0.0193        | 2.0   | 1300 | 0.0160          | 0.9282    | 0.9546 | 0.9412 | 0.9954   |
| 0.0149        | 3.0   | 1950 | 0.0142          | 0.9477    | 0.9577 | 0.9527 | 0.9964   |
| 0.0088        | 4.0   | 2600 | 0.0128          | 0.9551    | 0.9604 | 0.9577 | 0.9967   |
| 0.0069        | 5.0   | 3250 | 0.0135          | 0.9567    | 0.9635 | 0.9601 | 0.9968   |
| 0.0056        | 6.0   | 3900 | 0.0134          | 0.9552    | 0.9669 | 0.9610 | 0.9970   |
| 0.0037        | 7.0   | 4550 | 0.0137          | 0.9592    | 0.9688 | 0.9640 | 0.9971   |
| 0.0031        | 8.0   | 5200 | 0.0144          | 0.9592    | 0.9673 | 0.9632 | 0.9971   |
| 0.0026        | 9.0   | 5850 | 0.0157          | 0.9536    | 0.9711 | 0.9623 | 0.9970   |
| 0.0019        | 10.0  | 6500 | 0.0159          | 0.9586    | 0.9706 | 0.9646 | 0.9971   |
| 0.0016        | 11.0  | 7150 | 0.0163          | 0.9592    | 0.9711 | 0.9651 | 0.9972   |
| 0.0015        | 12.0  | 7800 | 0.0164          | 0.9621    | 0.9702 | 0.9661 | 0.9972   |
| 0.0013        | 13.0  | 8450 | 0.0166          | 0.9625    | 0.9714 | 0.9669 | 0.9973   |
| 0.001         | 14.0  | 9100 | 0.0171          | 0.9624    | 0.9711 | 0.9667 | 0.9973   |
| 0.0009        | 15.0  | 9750 | 0.0171          | 0.9610    | 0.9716 | 0.9663 | 0.9973   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1