pytorch tensorboard albert token-classification List of ISO 639-1 code for your language zh transformers autotrain_compatible
---
language: 
  - "List of ISO 639-1 code for your language"
  - zh
widget:
- text: "中央疫情指揮中心臨時記者會宣布全院區為紅區，擴大隔離，但鄭文燦早在七十二小時前就主張，只要是先前在桃園醫院住院、轉院的患者與陪病家屬，都要居家隔離"
  example_title: "範例ㄧ"
- text: "台東地檢署21日指揮警方前往張靜的事務所及黃姓女友所經營的按摩店進行搜索"
  example_title: "範例二"
- text: "各地停電事件頻傳，即便經濟部與台電均否認「台灣缺電」，但也難消國人的疑慮。"
  example_title: "範例三"

---
---
license: gpl-3.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-base-chinese-0407-ner
  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. -->

# albert-base-chinese-0407-ner

This model is a fine-tuned version of [ckiplab/albert-base-chinese](https://huggingface.co/ckiplab/albert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0948
- Precision: 0.8603
- Recall: 0.8871
- F1: 0.8735
- Accuracy: 0.9704

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3484        | 0.05  | 500   | 0.5395          | 0.1841    | 0.1976 | 0.1906 | 0.8465   |
| 0.3948        | 0.09  | 1000  | 0.2910          | 0.6138    | 0.7113 | 0.6590 | 0.9263   |
| 0.2388        | 0.14  | 1500  | 0.2030          | 0.6628    | 0.7797 | 0.7165 | 0.9414   |
| 0.1864        | 0.18  | 2000  | 0.1729          | 0.7490    | 0.7935 | 0.7706 | 0.9498   |
| 0.1754        | 0.23  | 2500  | 0.1641          | 0.7415    | 0.7869 | 0.7635 | 0.9505   |
| 0.1558        | 0.28  | 3000  | 0.1532          | 0.7680    | 0.8002 | 0.7838 | 0.9530   |
| 0.1497        | 0.32  | 3500  | 0.1424          | 0.7865    | 0.8282 | 0.8068 | 0.9555   |
| 0.1488        | 0.37  | 4000  | 0.1373          | 0.7887    | 0.8111 | 0.7997 | 0.9553   |
| 0.1361        | 0.42  | 4500  | 0.1311          | 0.7942    | 0.8382 | 0.8156 | 0.9590   |
| 0.1335        | 0.46  | 5000  | 0.1264          | 0.7948    | 0.8423 | 0.8179 | 0.9596   |
| 0.1296        | 0.51  | 5500  | 0.1242          | 0.8129    | 0.8416 | 0.8270 | 0.9603   |
| 0.1338        | 0.55  | 6000  | 0.1315          | 0.7910    | 0.8588 | 0.8235 | 0.9586   |
| 0.1267        | 0.6   | 6500  | 0.1193          | 0.8092    | 0.8399 | 0.8243 | 0.9609   |
| 0.1207        | 0.65  | 7000  | 0.1205          | 0.8021    | 0.8469 | 0.8239 | 0.9601   |
| 0.1214        | 0.69  | 7500  | 0.1201          | 0.7969    | 0.8489 | 0.8220 | 0.9605   |
| 0.1168        | 0.74  | 8000  | 0.1134          | 0.8087    | 0.8607 | 0.8339 | 0.9620   |
| 0.1162        | 0.78  | 8500  | 0.1127          | 0.8177    | 0.8492 | 0.8331 | 0.9625   |
| 0.1202        | 0.83  | 9000  | 0.1283          | 0.7986    | 0.8550 | 0.8259 | 0.9580   |
| 0.1135        | 0.88  | 9500  | 0.1101          | 0.8213    | 0.8572 | 0.8389 | 0.9638   |
| 0.1121        | 0.92  | 10000 | 0.1097          | 0.8190    | 0.8588 | 0.8384 | 0.9635   |
| 0.1091        | 0.97  | 10500 | 0.1088          | 0.8180    | 0.8521 | 0.8347 | 0.9632   |
| 0.1058        | 1.02  | 11000 | 0.1085          | 0.8136    | 0.8716 | 0.8416 | 0.9630   |
| 0.0919        | 1.06  | 11500 | 0.1079          | 0.8309    | 0.8566 | 0.8436 | 0.9646   |
| 0.0914        | 1.11  | 12000 | 0.1079          | 0.8423    | 0.8542 | 0.8482 | 0.9656   |
| 0.0921        | 1.15  | 12500 | 0.1109          | 0.8312    | 0.8647 | 0.8476 | 0.9646   |
| 0.0926        | 1.2   | 13000 | 0.1240          | 0.8413    | 0.8488 | 0.8451 | 0.9637   |
| 0.0914        | 1.25  | 13500 | 0.1040          | 0.8336    | 0.8666 | 0.8498 | 0.9652   |
| 0.0917        | 1.29  | 14000 | 0.1032          | 0.8352    | 0.8707 | 0.8526 | 0.9662   |
| 0.0928        | 1.34  | 14500 | 0.1052          | 0.8347    | 0.8656 | 0.8498 | 0.9651   |
| 0.0906        | 1.38  | 15000 | 0.1032          | 0.8399    | 0.8619 | 0.8507 | 0.9662   |
| 0.0903        | 1.43  | 15500 | 0.1074          | 0.8180    | 0.8708 | 0.8436 | 0.9651   |
| 0.0889        | 1.48  | 16000 | 0.0990          | 0.8367    | 0.8713 | 0.8537 | 0.9670   |
| 0.0914        | 1.52  | 16500 | 0.1055          | 0.8508    | 0.8506 | 0.8507 | 0.9661   |
| 0.0934        | 1.57  | 17000 | 0.0979          | 0.8326    | 0.8740 | 0.8528 | 0.9669   |
| 0.0898        | 1.62  | 17500 | 0.1022          | 0.8393    | 0.8615 | 0.8502 | 0.9668   |
| 0.0869        | 1.66  | 18000 | 0.0962          | 0.8484    | 0.8762 | 0.8621 | 0.9682   |
| 0.089         | 1.71  | 18500 | 0.1008          | 0.8447    | 0.8714 | 0.8579 | 0.9674   |
| 0.0927        | 1.75  | 19000 | 0.0986          | 0.8379    | 0.8749 | 0.8560 | 0.9673   |
| 0.0883        | 1.8   | 19500 | 0.0965          | 0.8518    | 0.8749 | 0.8632 | 0.9688   |
| 0.0965        | 1.85  | 20000 | 0.0937          | 0.8412    | 0.8766 | 0.8585 | 0.9682   |
| 0.0834        | 1.89  | 20500 | 0.0920          | 0.8451    | 0.8862 | 0.8652 | 0.9687   |
| 0.0817        | 1.94  | 21000 | 0.0943          | 0.8439    | 0.8800 | 0.8616 | 0.9686   |
| 0.088         | 1.99  | 21500 | 0.0927          | 0.8483    | 0.8762 | 0.8620 | 0.9683   |
| 0.0705        | 2.03  | 22000 | 0.0993          | 0.8525    | 0.8783 | 0.8652 | 0.9690   |
| 0.0709        | 2.08  | 22500 | 0.0976          | 0.8610    | 0.8697 | 0.8653 | 0.9689   |
| 0.0655        | 2.12  | 23000 | 0.0997          | 0.8585    | 0.8665 | 0.8625 | 0.9683   |
| 0.0656        | 2.17  | 23500 | 0.0966          | 0.8569    | 0.8822 | 0.8694 | 0.9695   |
| 0.0698        | 2.22  | 24000 | 0.0955          | 0.8604    | 0.8775 | 0.8689 | 0.9696   |
| 0.065         | 2.26  | 24500 | 0.0971          | 0.8614    | 0.8780 | 0.8696 | 0.9697   |
| 0.0653        | 2.31  | 25000 | 0.0959          | 0.8600    | 0.8787 | 0.8692 | 0.9698   |
| 0.0685        | 2.35  | 25500 | 0.1001          | 0.8610    | 0.8710 | 0.8659 | 0.9690   |
| 0.0684        | 2.4   | 26000 | 0.0969          | 0.8490    | 0.8877 | 0.8679 | 0.9690   |
| 0.0657        | 2.45  | 26500 | 0.0954          | 0.8532    | 0.8832 | 0.8680 | 0.9696   |
| 0.0668        | 2.49  | 27000 | 0.0947          | 0.8604    | 0.8793 | 0.8698 | 0.9695   |
| 0.0644        | 2.54  | 27500 | 0.0989          | 0.8527    | 0.8790 | 0.8656 | 0.9696   |
| 0.0685        | 2.59  | 28000 | 0.0955          | 0.8596    | 0.8772 | 0.8683 | 0.9700   |
| 0.0702        | 2.63  | 28500 | 0.0937          | 0.8585    | 0.8837 | 0.8709 | 0.9700   |
| 0.0644        | 2.68  | 29000 | 0.0946          | 0.8605    | 0.8830 | 0.8716 | 0.9702   |
| 0.065         | 2.72  | 29500 | 0.0953          | 0.8617    | 0.8822 | 0.8719 | 0.9701   |
| 0.063         | 2.77  | 30000 | 0.0943          | 0.8597    | 0.8848 | 0.8721 | 0.9701   |
| 0.0638        | 2.82  | 30500 | 0.0941          | 0.8619    | 0.8846 | 0.8731 | 0.9702   |
| 0.066         | 2.86  | 31000 | 0.0942          | 0.8608    | 0.8847 | 0.8726 | 0.9701   |
| 0.0589        | 2.91  | 31500 | 0.0952          | 0.8632    | 0.8836 | 0.8733 | 0.9704   |
| 0.0568        | 2.95  | 32000 | 0.0948          | 0.8603    | 0.8871 | 0.8735 | 0.9704   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
