readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - ner-tr-qa
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-ner-qa
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ner-tr-qa
        type: ner-tr-qa
        config: NERTR
        split: train
        args: NERTR
      metrics:
      - name: Precision
        type: precision
        value: 0.9946695095948828
      - name: Recall
        type: recall
        value: 0.9899204244031831
      - name: F1
        type: f1
        value: 0.9922892847646904
      - name: Accuracy
        type: accuracy
        value: 0.9977653631284916
  ---
  
  <!-- 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. -->
  
  # distilbert-ner-qa
  
  This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the ner-tr-qa dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0115
  - Precision: 0.9947
  - Recall: 0.9899
  - F1: 0.9923
  - Accuracy: 0.9978
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3281        | 1.0   | 576  | 0.0426          | 0.9594    | 0.9538 | 0.9566 | 0.9906   |
  | 0.0379        | 2.0   | 1152 | 0.0165          | 0.9899    | 0.9846 | 0.9872 | 0.9966   |
  | 0.0212        | 3.0   | 1728 | 0.0115          | 0.9947    | 0.9899 | 0.9923 | 0.9978   |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  