readme: ---
  license: mit
  tags:
  - generated_from_trainer
  - BACnet
  metrics:
  - f1
  language:
  - de
  widget:
  - text: "11004KAE901KL1ST15"
  - text: "Heizkreis Nord Ost"
  - text: "Raumluftqualitaet RLT Sporthalle"
  - text: "11004ELT002IS011MW22"
  - text: "Beschreibung: Abschaltung durch Stoppwert erreicht KM 1; Name: 11004KAE901KL1ST15'"
  model-index:
  - name: BACnet-Klassifizierung-Gewerke
    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-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_gewerke" dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0394
  - F1: [0.96296296 0.8        0.97297297 1.         0.99469027 0.98979592  0.98969072]
  
  ## Model description
  
  This model makes it possible to classify the components of the technical building equipment described with the BACnet standard into different trades. 
  The model is based on a German-language data set.
  
  ## Intended uses & limitations
  
  The model divides descriptive texts into the following building services trades:
  Waste_water_water_gas_systems, Other_systems, Building_automation, Refrigeration_systems, Air_technical_systems, Heavy_current_systems and Heat_supply_systems
  
  ## 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: 5.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1                                                                              |
  |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|
  | 0.4309        | 0.99  | 45   | 0.0736          | [0.89655172 0.84210526 0.97297297 0.98901099 0.9929078  0.99492386 0.98701299] |
  | 0.0722        | 1.99  | 90   | 0.0511          | [0.92307692 0.875      0.96       1.         0.99295775 0.98979592 0.98714653] |
  | 0.0431        | 2.99  | 135  | 0.0460          | [1.         0.8        0.97297297 1.         0.99469027 0.98979592 0.99224806] |
  | 0.0313        | 3.99  | 180  | 0.0365          | [1.         0.84210526 0.97297297 1.         0.99646643 0.98979592 0.99224806] |
  | 0.0238        | 4.99  | 225  | 0.0394          | [0.96296296 0.8        0.97297297 1.         0.99469027 0.98979592 0.98969072] |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  