readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: bert-base-german-cased-gnad10-finetuned-tagesschau-subcategories
    results: []
  ---
  
  <!-- 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-german-cased-gnad10-finetuned-tagesschau-subcategories
  
  This model is a fine-tuned version of [Mathking/bert-base-german-cased-gnad10](https://huggingface.co/Mathking/bert-base-german-cased-gnad10) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4327
  - Accuracy: 0.8733
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.3737        | 0.4   | 30   | 0.9333          | 0.7133   |
  | 0.7848        | 0.8   | 60   | 0.5591          | 0.8133   |
  | 0.4933        | 1.2   | 90   | 0.4939          | 0.8      |
  | 0.3441        | 1.6   | 120  | 0.5537          | 0.8133   |
  | 0.3972        | 2.0   | 150  | 0.4229          | 0.8533   |
  | 0.2103        | 2.4   | 180  | 0.4327          | 0.8733   |
  | 0.1783        | 2.8   | 210  | 0.4834          | 0.8467   |
  | 0.1367        | 3.2   | 240  | 0.4634          | 0.86     |
  | 0.1273        | 3.6   | 270  | 0.4828          | 0.8467   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  