readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  language:
  - de
  model-index:
  - name: BACnet-Klassifizierung-Sanitaertechnik-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-Sanitaertechnik-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_sanitaer_v2" dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0039
  - F1: [1. 1. 1.]
  
  ## Model description
  
  This model makes it possible to classify the sanitary technology 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 sanitary engineering categories:
  Other, pressure boosting system, softening system, lifting system, sanitary_general, waste water, drinking water heating system and water meter.
  
  ## Training and evaluation data
  
  The model is based on a German-language data set.
  
  ## 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: 40.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1         |
  |:-------------:|:-----:|:----:|:---------------:|:----------:|
  | 0.0507        | 1.0   | 1    | 0.1080          | [1. 1. 1.] |
  | 0.0547        | 2.0   | 2    | 0.0589          | [1. 1. 1.] |
  | 0.0407        | 3.0   | 3    | 0.0427          | [1. 1. 1.] |
  | 0.0294        | 4.0   | 4    | 0.0465          | [1. 1. 1.] |
  | 0.0284        | 5.0   | 5    | 0.0291          | [1. 1. 1.] |
  | 0.0208        | 6.0   | 6    | 0.0232          | [1. 1. 1.] |
  | 0.0171        | 7.0   | 7    | 0.0198          | [1. 1. 1.] |
  | 0.0153        | 8.0   | 8    | 0.0170          | [1. 1. 1.] |
  | 0.0134        | 9.0   | 9    | 0.0144          | [1. 1. 1.] |
  | 0.0126        | 10.0  | 10   | 0.0124          | [1. 1. 1.] |
  | 0.0108        | 11.0  | 11   | 0.0109          | [1. 1. 1.] |
  | 0.0096        | 12.0  | 12   | 0.0098          | [1. 1. 1.] |
  | 0.0084        | 13.0  | 13   | 0.0089          | [1. 1. 1.] |
  | 0.0082        | 14.0  | 14   | 0.0083          | [1. 1. 1.] |
  | 0.0071        | 15.0  | 15   | 0.0077          | [1. 1. 1.] |
  | 0.0068        | 16.0  | 16   | 0.0073          | [1. 1. 1.] |
  | 0.0064        | 17.0  | 17   | 0.0069          | [1. 1. 1.] |
  | 0.0059        | 18.0  | 18   | 0.0065          | [1. 1. 1.] |
  | 0.0053        | 19.0  | 19   | 0.0061          | [1. 1. 1.] |
  | 0.0052        | 20.0  | 20   | 0.0058          | [1. 1. 1.] |
  | 0.005         | 21.0  | 21   | 0.0056          | [1. 1. 1.] |
  | 0.0047        | 22.0  | 22   | 0.0053          | [1. 1. 1.] |
  | 0.0044        | 23.0  | 23   | 0.0051          | [1. 1. 1.] |
  | 0.0042        | 24.0  | 24   | 0.0050          | [1. 1. 1.] |
  | 0.0043        | 25.0  | 25   | 0.0048          | [1. 1. 1.] |
  | 0.004         | 26.0  | 26   | 0.0047          | [1. 1. 1.] |
  | 0.004         | 27.0  | 27   | 0.0045          | [1. 1. 1.] |
  | 0.004         | 28.0  | 28   | 0.0044          | [1. 1. 1.] |
  | 0.0037        | 29.0  | 29   | 0.0044          | [1. 1. 1.] |
  | 0.0037        | 30.0  | 30   | 0.0043          | [1. 1. 1.] |
  | 0.0037        | 31.0  | 31   | 0.0042          | [1. 1. 1.] |
  | 0.0035        | 32.0  | 32   | 0.0042          | [1. 1. 1.] |
  | 0.0036        | 33.0  | 33   | 0.0041          | [1. 1. 1.] |
  | 0.0035        | 34.0  | 34   | 0.0041          | [1. 1. 1.] |
  | 0.0037        | 35.0  | 35   | 0.0040          | [1. 1. 1.] |
  | 0.0034        | 36.0  | 36   | 0.0040          | [1. 1. 1.] |
  | 0.0033        | 37.0  | 37   | 0.0040          | [1. 1. 1.] |
  | 0.0034        | 38.0  | 38   | 0.0040          | [1. 1. 1.] |
  | 0.0034        | 39.0  | 39   | 0.0040          | [1. 1. 1.] |
  | 0.0034        | 40.0  | 40   | 0.0039          | [1. 1. 1.] |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  