readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  language:
  - de
  model-index:
  - name: BACnet-Klassifizierung-Kaeltettechnik-bert-base-german-cased
    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-Kaeltettechnik-bert-base-german-cased
  
  This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the [gart-labor](https://huggingface.co/gart-labor) "klassifizierung_kaelte_v2" dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0466
  - F1: [0.85714286 0.98507463 1.         1.        ]
  
  ## Model description
  
  This model makes it possible to classify the refrigeration components described with the BACnet standard into different categories. 
  The model is based on a German-language data set.
  
  ## Intended uses & limitations
  
  The model divides descriptive texts into the following refrigeration categories:
  Free_Cooling, Refrigeration_General, Chiller, Cold Storage and Recooling Plant
  
  ## Training and evaluation data
  
  The model is based on a German-language data set.
  
  ### 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: 8.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1                                            |
  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------:|
  | 0.0426        | 0.85  | 5    | 0.0439          | [0.85714286 0.98507463 1.         1.        ] |
  | 0.0175        | 1.85  | 10   | 0.0466          | [0.85714286 0.98507463 1.         1.        ] |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  