readme: ---
  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  