---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: chinese-address-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9852459016393442
---

<!-- 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. -->

# chinese-address-ner

This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0999
- Precision: 0.9739
- Recall: 0.9849
- F1: 0.9794
- Accuracy: 0.9852

## 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: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0656        | 0.14  | 1    | 0.1061          | 0.9665    | 0.9811 | 0.9738 | 0.9844   |
| 0.1305        | 0.29  | 2    | 0.1096          | 0.9630    | 0.9811 | 0.9720 | 0.9836   |
| 0.1009        | 0.43  | 3    | 0.0999          | 0.9739    | 0.9849 | 0.9794 | 0.9852   |
| 0.0844        | 0.57  | 4    | 0.0911          | 0.9739    | 0.9849 | 0.9794 | 0.9852   |
| 0.0773        | 0.71  | 5    | 0.0858          | 0.9703    | 0.9849 | 0.9775 | 0.9852   |
| 0.0997        | 0.86  | 6    | 0.0815          | 0.9739    | 0.9849 | 0.9794 | 0.9861   |
| 0.0904        | 1.0   | 7    | 0.0795          | 0.9739    | 0.9849 | 0.9794 | 0.9861   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.7.0
- Datasets 1.9.0
- Tokenizers 0.10.3