readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-cased-tajik-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        config: tg
        split: train+test
        args: tg
      metrics:
      - name: Precision
        type: precision
        value: 0.512396694214876
      - name: Recall
        type: recall
        value: 0.5961538461538461
      - name: F1
        type: f1
        value: 0.5511111111111111
      - name: Accuracy
        type: accuracy
        value: 0.8520825223822499
  ---
  
  <!-- 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-base-cased-tajik-ner
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1137
  - Precision: 0.5124
  - Recall: 0.5962
  - F1: 0.5511
  - Accuracy: 0.8521
  
  ## 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: 200
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 2.0   | 50   | 0.8416          | 0.0739    | 0.125  | 0.0929 | 0.6948   |
  | No log        | 4.0   | 100  | 0.7061          | 0.2229    | 0.3558 | 0.2741 | 0.7415   |
  | No log        | 6.0   | 150  | 0.6467          | 0.3057    | 0.4615 | 0.3678 | 0.8167   |
  | No log        | 8.0   | 200  | 0.7923          | 0.3968    | 0.4808 | 0.4348 | 0.8073   |
  | No log        | 10.0  | 250  | 0.7003          | 0.4656    | 0.5865 | 0.5191 | 0.8653   |
  | No log        | 12.0  | 300  | 0.7723          | 0.4380    | 0.5769 | 0.4979 | 0.8560   |
  | No log        | 14.0  | 350  | 0.9088          | 0.4762    | 0.5769 | 0.5217 | 0.8470   |
  | No log        | 16.0  | 400  | 0.9756          | 0.472     | 0.5673 | 0.5153 | 0.8424   |
  | No log        | 18.0  | 450  | 1.1114          | 0.4576    | 0.5192 | 0.4865 | 0.8151   |
  | 0.2358        | 20.0  | 500  | 1.0887          | 0.48      | 0.5769 | 0.5240 | 0.8330   |
  | 0.2358        | 22.0  | 550  | 1.0968          | 0.4419    | 0.5481 | 0.4893 | 0.8268   |
  | 0.2358        | 24.0  | 600  | 1.3330          | 0.5140    | 0.5288 | 0.5213 | 0.8042   |
  | 0.2358        | 26.0  | 650  | 1.0911          | 0.6019    | 0.5962 | 0.5990 | 0.8521   |
  | 0.2358        | 28.0  | 700  | 1.1949          | 0.4586    | 0.5865 | 0.5148 | 0.8388   |
  | 0.2358        | 30.0  | 750  | 1.1208          | 0.4444    | 0.5769 | 0.5021 | 0.8470   |
  | 0.2358        | 32.0  | 800  | 1.0968          | 0.5413    | 0.5673 | 0.5540 | 0.8661   |
  | 0.2358        | 34.0  | 850  | 1.1618          | 0.5       | 0.5769 | 0.5357 | 0.8575   |
  | 0.2358        | 36.0  | 900  | 1.1018          | 0.5169    | 0.5865 | 0.5495 | 0.8505   |
  | 0.2358        | 38.0  | 950  | 1.1948          | 0.4797    | 0.5673 | 0.5198 | 0.8431   |
  | 0.0039        | 40.0  | 1000 | 1.1063          | 0.4511    | 0.5769 | 0.5063 | 0.8533   |
  | 0.0039        | 42.0  | 1050 | 1.0651          | 0.5702    | 0.625  | 0.5963 | 0.8723   |
  | 0.0039        | 44.0  | 1100 | 1.1475          | 0.472     | 0.5673 | 0.5153 | 0.8466   |
  | 0.0039        | 46.0  | 1150 | 1.3080          | 0.4590    | 0.5385 | 0.4956 | 0.8353   |
  | 0.0039        | 48.0  | 1200 | 1.1165          | 0.5741    | 0.5962 | 0.5849 | 0.8610   |
  | 0.0039        | 50.0  | 1250 | 1.2525          | 0.4724    | 0.5769 | 0.5195 | 0.8431   |
  | 0.0039        | 52.0  | 1300 | 1.2443          | 0.5161    | 0.6154 | 0.5614 | 0.8521   |
  | 0.0039        | 54.0  | 1350 | 1.5720          | 0.4597    | 0.5481 | 0.5    | 0.8054   |
  | 0.0039        | 56.0  | 1400 | 1.2487          | 0.5446    | 0.5865 | 0.5648 | 0.8513   |
  | 0.0039        | 58.0  | 1450 | 1.3936          | 0.4754    | 0.5577 | 0.5133 | 0.8365   |
  | 0.0051        | 60.0  | 1500 | 1.2980          | 0.5636    | 0.5962 | 0.5794 | 0.8544   |
  | 0.0051        | 62.0  | 1550 | 1.3284          | 0.5175    | 0.5673 | 0.5413 | 0.8490   |
  | 0.0051        | 64.0  | 1600 | 1.3345          | 0.5268    | 0.5673 | 0.5463 | 0.8447   |
  | 0.0051        | 66.0  | 1650 | 1.1006          | 0.5872    | 0.6154 | 0.6009 | 0.8641   |
  | 0.0051        | 68.0  | 1700 | 1.0886          | 0.4580    | 0.5769 | 0.5106 | 0.8525   |
  | 0.0051        | 70.0  | 1750 | 1.1017          | 0.4959    | 0.5865