readme: ---
  widget:
  - text: " Исмоили Сомонӣ - намояндаи бузурги форсу-тоҷик" 
  - text: "Ин фурудгоҳ дар кишвари Индонезия қарор дорад."
  - text: " Бобоҷон Ғафуров – солҳои 1946-1956"
  - text: " Лоиқ Шералӣ дар васфи Модар шеър"
  
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: tajberto-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: 0.576
      - name: Recall
        type: recall
        value: 0.6923076923076923
      - name: F1
        type: f1
        value: 0.62882096069869
      - name: Accuracy
        type: accuracy
        value: 0.8934049079754601
  ---
  
  <!-- 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. -->
  
  # tajberto-ner
  
  This model is a fine-tuned version of [muhtasham/TajBERTo](https://huggingface.co/muhtasham/TajBERTo) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6129
  - Precision: 0.576
  - Recall: 0.6923
  - F1: 0.6288
  - Accuracy: 0.8934
  
  ## 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   | 0.6171          | 0.1667    | 0.2885 | 0.2113 | 0.7646   |
  | No log        | 4.0   | 100  | 0.4733          | 0.2824    | 0.4615 | 0.3504 | 0.8344   |
  | No log        | 6.0   | 150  | 0.3857          | 0.3372    | 0.5577 | 0.4203 | 0.8589   |
  | No log        | 8.0   | 200  | 0.4523          | 0.4519    | 0.5865 | 0.5105 | 0.8765   |
  | No log        | 10.0  | 250  | 0.3870          | 0.44      | 0.6346 | 0.5197 | 0.8834   |
  | No log        | 12.0  | 300  | 0.4512          | 0.5267    | 0.6635 | 0.5872 | 0.8865   |
  | No log        | 14.0  | 350  | 0.4934          | 0.4789    | 0.6538 | 0.5528 | 0.8819   |
  | No log        | 16.0  | 400  | 0.4924          | 0.4783    | 0.6346 | 0.5455 | 0.8842   |
  | No log        | 18.0  | 450  | 0.5355          | 0.4595    | 0.6538 | 0.5397 | 0.8788   |
  | 0.1682        | 20.0  | 500  | 0.5440          | 0.5547    | 0.6827 | 0.6121 | 0.8942   |
  | 0.1682        | 22.0  | 550  | 0.5299          | 0.5794    | 0.7019 | 0.6348 | 0.9003   |
  | 0.1682        | 24.0  | 600  | 0.5735          | 0.5691    | 0.6731 | 0.6167 | 0.8926   |
  | 0.1682        | 26.0  | 650  | 0.6027          | 0.5833    | 0.6731 | 0.6250 | 0.8796   |
  | 0.1682        | 28.0  | 700  | 0.6119          | 0.568     | 0.6827 | 0.6201 | 0.8934   |
  | 0.1682        | 30.0  | 750  | 0.6098          | 0.5635    | 0.6827 | 0.6174 | 0.8911   |
  | 0.1682        | 32.0  | 800  | 0.6237          | 0.5469    | 0.6731 | 0.6034 | 0.8834   |
  | 0.1682        | 34.0  | 850  | 0.6215          | 0.5530    | 0.7019 | 0.6186 | 0.8842   |
  | 0.1682        | 36.0  | 900  | 0.6179          | 0.5802    | 0.7308 | 0.6468 | 0.8888   |
  | 0.1682        | 38.0  | 950  | 0.6201          | 0.5373    | 0.6923 | 0.6050 | 0.8873   |
  | 0.0007        | 40.0  | 1000 | 0.6114          | 0.5952    | 0.7212 | 0.6522 | 0.8911   |
  | 0.0007        | 42.0  | 1050 | 0.6073          | 0.5625    | 0.6923 | 0.6207 | 0.8896   |
  | 0.0007        | 44.0  | 1100 | 0.6327          | 0.5620    | 0.6538 | 0.6044 | 0.8896   |
  | 0.0007        | 46.0  | 1150 | 0.6129          | 0.576     | 0.6923 | 0.6288 | 0.8934   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  