readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - skript
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-ner-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: skript
        type: skript
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.058091286307053944
      - name: Recall
        type: recall
        value: 0.04498714652956298
      - name: F1
        type: f1
        value: 0.05070626584570808
      - name: Accuracy
        type: accuracy
        value: 0.7974446689319497
  ---
  
  <!-- 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-base-uncased-finetuned-ner-finetuned-ner
  
  This model was trained from scratch on the skript dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6713
  - Precision: 0.0581
  - Recall: 0.0450
  - F1: 0.0507
  - Accuracy: 0.7974
  
  ## 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: 16
  - eval_batch_size: 16
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 44   | 0.8207          | 0.0       | 0.0    | 0.0    | 0.7748   |
  | No log        | 2.0   | 88   | 0.7113          | 0.0405    | 0.0231 | 0.0294 | 0.7889   |
  | No log        | 3.0   | 132  | 0.6713          | 0.0581    | 0.0450 | 0.0507 | 0.7974   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  