---
tags:
- generated_from_trainer
datasets:
- peoples_daily_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-chinese-people-daily
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: peoples_daily_ner
      type: peoples_daily_ner
      config: peoples_daily_ner
      split: validation
      args: peoples_daily_ner
    metrics:
    - name: Precision
      type: precision
      value: 0.8608247422680413
    - name: Recall
      type: recall
      value: 0.8608247422680413
    - name: F1
      type: f1
      value: 0.8608247422680413
    - name: Accuracy
      type: accuracy
      value: 0.9852778800147222
---

<!-- 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-finetuned-ner-chinese-people-daily

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the peoples_daily_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Precision: 0.8608
- Recall: 0.8608
- F1: 0.8608
- Accuracy: 0.9853

## 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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 131  | 0.0753          | 0.6955    | 0.7887 | 0.7391 | 0.9764   |
| No log        | 2.0   | 262  | 0.0588          | 0.7971    | 0.8505 | 0.8229 | 0.9840   |
| No log        | 3.0   | 393  | 0.0604          | 0.8608    | 0.8608 | 0.8608 | 0.9853   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3