readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - movie_releases
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-radarr
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: movie_releases
        type: movie_releases
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.9555421444377389
      - name: Recall
        type: recall
        value: 0.9638798701298701
      - name: F1
        type: f1
        value: 0.9596928982725529
      - name: Accuracy
        type: accuracy
        value: 0.9817602584524263
  ---
  
  <!-- 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-finetuned-radarr
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the movie_releases dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0731
  - Precision: 0.9555
  - Recall: 0.9639
  - F1: 0.9597
  - Accuracy: 0.9818
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0431        | 1.0   | 1191 | 0.1403          | 0.9436    | 0.9574 | 0.9504 | 0.9626   |
  | 0.0236        | 2.0   | 2382 | 0.0881          | 0.9485    | 0.9560 | 0.9522 | 0.9694   |
  | 0.0138        | 3.0   | 3573 | 0.0731          | 0.9555    | 0.9639 | 0.9597 | 0.9818   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  