---
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- precision
- recall
- f1
model-index:
- name: NERBorder
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.901610712050607
    - name: Recall
      type: recall
      value: 0.8982985303950894
    - name: F1
      type: f1
      value: 0.8999515736949341
---

<!-- 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. -->

# NERBorder

This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5195
- Precision: 0.9016
- Recall: 0.8983
- F1: 0.9000

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.2099        | 1.0   | 416  | 0.1940          | 0.8281    | 0.8152 | 0.8216 |
| 0.1658        | 2.0   | 832  | 0.1799          | 0.8464    | 0.8590 | 0.8527 |
| 0.1276        | 3.0   | 1248 | 0.1821          | 0.8795    | 0.8639 | 0.8716 |
| 0.1076        | 4.0   | 1664 | 0.1961          | 0.8903    | 0.8788 | 0.8845 |
| 0.0792        | 5.0   | 2080 | 0.2277          | 0.8787    | 0.8869 | 0.8828 |
| 0.054         | 6.0   | 2496 | 0.2395          | 0.9084    | 0.8701 | 0.8888 |
| 0.0433        | 7.0   | 2912 | 0.2991          | 0.8999    | 0.8915 | 0.8957 |
| 0.0288        | 8.0   | 3328 | 0.3374          | 0.8919    | 0.8935 | 0.8927 |
| 0.022         | 9.0   | 3744 | 0.3752          | 0.9054    | 0.8921 | 0.8987 |
| 0.0211        | 10.0  | 4160 | 0.4105          | 0.8952    | 0.8985 | 0.8968 |
| 0.0147        | 11.0  | 4576 | 0.4084          | 0.9013    | 0.9004 | 0.9009 |
| 0.0095        | 12.0  | 4992 | 0.4542          | 0.9047    | 0.8952 | 0.8999 |
| 0.01          | 13.0  | 5408 | 0.4516          | 0.9086    | 0.8896 | 0.8990 |
| 0.0087        | 14.0  | 5824 | 0.4521          | 0.9025    | 0.8935 | 0.8980 |
| 0.0069        | 15.0  | 6240 | 0.4878          | 0.9034    | 0.9022 | 0.9028 |
| 0.0042        | 16.0  | 6656 | 0.5097          | 0.9021    | 0.8997 | 0.9009 |
| 0.006         | 17.0  | 7072 | 0.5195          | 0.9054    | 0.9008 | 0.9031 |
| 0.0043        | 18.0  | 7488 | 0.5032          | 0.9009    | 0.8977 | 0.8993 |
| 0.0029        | 19.0  | 7904 | 0.5155          | 0.9003    | 0.8962 | 0.8983 |
| 0.0034        | 20.0  | 8320 | 0.5195          | 0.9016    | 0.8983 | 0.9000 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0