readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: BACnet-Klassifizierung-Gewerke-2.0
    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. -->
  
  # BACnet-Klassifizierung-Gewerke-2.0
  
  This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0454
  - F1: [0.92307692 0.84210526 0.97297297 1.         0.99469027 0.98979592
   0.98974359]
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1                                                                              |
  |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|
  | 0.4695        | 0.99  | 45   | 0.0885          | [0.93333333 0.875      0.96       0.97727273 0.99121265 0.98492462
   0.98701299] |
  | 0.0617        | 1.99  | 90   | 0.0619          | [0.81481481 0.875      0.96       1.         0.99646643 0.99492386
   0.98200514] |
  | 0.0407        | 2.99  | 135  | 0.0482          | [1.         0.84210526 0.97297297 1.         0.99469027 0.98979592
   0.99484536] |
  | 0.0267        | 3.99  | 180  | 0.0460          | [0.96551724 0.77777778 0.97297297 1.         0.99469027 0.98979592
   0.99484536] |
  | 0.0193        | 4.99  | 225  | 0.0454          | [0.92307692 0.84210526 0.97297297 1.         0.99469027 0.98979592
   0.98974359] |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  