---
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: 0.975825946817083
---

<!-- 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: 0.1080
- Precision: 0.9664
- Recall: 0.9774
- F1: 0.9719
- Accuracy: 0.9758

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.5055        | 1.0   | 7    | 1.6719          | 0.1977    | 0.2604 | 0.2248 | 0.5649   |
| 1.837         | 2.0   | 14   | 1.0719          | 0.4676    | 0.6    | 0.5256 | 0.7421   |
| 1.0661        | 3.0   | 21   | 0.7306          | 0.6266    | 0.7472 | 0.6816 | 0.8106   |
| 0.8373        | 4.0   | 28   | 0.5197          | 0.6456    | 0.8113 | 0.7191 | 0.8614   |
| 0.522         | 5.0   | 35   | 0.3830          | 0.7667    | 0.8679 | 0.8142 | 0.9001   |
| 0.4295        | 6.0   | 42   | 0.3104          | 0.8138    | 0.8906 | 0.8505 | 0.9178   |
| 0.3483        | 7.0   | 49   | 0.2453          | 0.8462    | 0.9132 | 0.8784 | 0.9404   |
| 0.2471        | 8.0   | 56   | 0.2081          | 0.8403    | 0.9132 | 0.8752 | 0.9428   |
| 0.2299        | 9.0   | 63   | 0.1979          | 0.8419    | 0.9245 | 0.8813 | 0.9420   |
| 0.1761        | 10.0  | 70   | 0.1823          | 0.8830    | 0.9396 | 0.9104 | 0.9500   |
| 0.1434        | 11.0  | 77   | 0.1480          | 0.9036    | 0.9547 | 0.9284 | 0.9629   |
| 0.134         | 12.0  | 84   | 0.1341          | 0.9173    | 0.9623 | 0.9392 | 0.9678   |
| 0.128         | 13.0  | 91   | 0.1365          | 0.9375    | 0.9623 | 0.9497 | 0.9694   |
| 0.0824        | 14.0  | 98   | 0.1159          | 0.9557    | 0.9774 | 0.9664 | 0.9734   |
| 0.0744        | 15.0  | 105  | 0.1092          | 0.9591    | 0.9736 | 0.9663 | 0.9766   |
| 0.0569        | 16.0  | 112  | 0.1117          | 0.9556    | 0.9736 | 0.9645 | 0.9742   |
| 0.0559        | 17.0  | 119  | 0.1040          | 0.9628    | 0.9774 | 0.9700 | 0.9790   |
| 0.0456        | 18.0  | 126  | 0.1052          | 0.9593    | 0.9774 | 0.9682 | 0.9782   |
| 0.0405        | 19.0  | 133  | 0.1133          | 0.9590    | 0.9698 | 0.9644 | 0.9718   |
| 0.0315        | 20.0  | 140  | 0.1060          | 0.9591    | 0.9736 | 0.9663 | 0.9750   |
| 0.0262        | 21.0  | 147  | 0.1087          | 0.9554    | 0.9698 | 0.9625 | 0.9718   |
| 0.0338        | 22.0  | 154  | 0.1183          | 0.9625    | 0.9698 | 0.9662 | 0.9726   |
| 0.0225        | 23.0  | 161  | 0.1080          | 0.9664    | 0.9774 | 0.9719 | 0.9758   |
| 0.028         | 24.0  | 168  | 0.1057          | 0.9591    | 0.9736 | 0.9663 | 0.9742   |
| 0.0202        | 25.0  | 175  | 0.1062          | 0.9628    | 0.9774 | 0.9700 | 0.9766   |
| 0.0168        | 26.0  | 182  | 0.1097          | 0.9664    | 0.9774 | 0.9719 | 0.9758   |
| 0.0173        | 27.0  | 189  | 0.1093          | 0.9628    | 0.9774 | 0.9700 | 0.9774   |
| 0.0151        | 28.0  | 196  | 0.1162          | 0.9628    | 0.9774 | 0.9700 | 0.9766   |
| 0.0135        | 29.0  | 203  | 0.1126          | 0.9483    | 0.9698 | 0.9590 | 0.9758   |
| 0.0179        | 30.0  | 210  | 0.1100          | 0.9449    | 0.9698 | 0.9572 | 0.9774   |
| 0.0161        | 31.0  | 217  | 0.1098          | 0.9449    | 0.9698 | 0.9572 | 0.9766   |
| 0.0158        | 32.0  | 224  | 0.1191          | 0.9483    | 0.9698 | 0.9590 | 0.9734   |
| 0.0151        | 33.0  | 231  | 0.1058          | 0.9483    | 0.9698 | 0.9590 | 0.9750   |
| 0.0121        | 34.0  | 238  | 0.0990          | 0.9593    | 0.9774 | 0.9682 | 0.9790   |
| 0.0092        | 35.0  | 245  | 0.1128          | 0.9519    | 0.9698 | 0.9607 | 0.9774   |
| 0.0097        | 36.0  | 252  | 0.1181          | 0.9627    | 0.9736 | 0.9681 | 0.9766   |
| 0.0118        | 37.0  | 259  | 0.1185          | 0.9591    | 0.9736 | 0.9663 | 0.9782   |
| 0.0118        | 38.0  | 266  | 0.1021          | 0.9557    | 0.9774 | 0.9664 | 0.9823   |
| 0.0099        | 39.0  | 273  | 0.1000          | 0.9559    | 0.9811 | 0.9683 | 0.9815   |
| 0.0102        | 40.0  | 280  | 0.1025          | 0.9559    | 0.9811 | 0.9683 | 0.9815   |
| 0.0068        | 41.0  | 287  | 0.1080          | 0.9522    | 0.9774 | 0.9646 | 0.9807   |
| 0.0105        | 42.0  | 294  | 0.1157          | 0.9449    | 0.9698 | 0.9572 | 0.9766   |
| 0.0083        | 43.0  | 301  | 0.1207          | 0.9380    | 0.9698 | 0.9536 | 0.9766   |
| 0.0077        | 44.0  | 308  | 0.1208          | 0.9483    | 0.9698 | 0.9590 | 0.9766   |
| 0.0077        | 45.0  | 315  | 0.1176          | 0.9483    | 0.9698 | 0.9590 | 0.9774   |
| 0.0071        | 46.0  | 322  | 0.1137          | 0.9483    | 0.9698 | 0.9590 | 0.9790   |
| 0.0075        | 47.0  | 329  | 0.1144          | 0.9483    | 0.9698 | 0.9590 | 0.9782   |
| 0.0084        | 48.0  | 336  | 0.1198          | 0.9483    | 0.9698 | 0.9590 | 0.9766   |
| 0.0103        | 49.0  | 343  | 0.1217          | 0.9519    | 0.9698 | 0.9607 | 0.9766   |
| 0.0087        | 50.0  | 350  | 0.1230          | 0.9519    | 0.9698 | 0.9607 | 0.9766   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.0
- Datasets 1.9.0
- Tokenizers 0.10.3