readme: ---
  license: mit
  widget:
  - text: "generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert."
  language:
  - de
  tags:
  - question generation
  datasets:
  - deepset/germanquad
  model-index:
  - name: german-qg-t5-drink600
    results: []
  ---
  
  # german-qg-t5-drink600
  
  This model is fine-tuned in question generation in German. The expected answer must be highlighted with &lt;hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions.
  
  ## Task example
  
  #### Input
  
  generate question: Der Monk Sour Drink ist ein somit eine aromatische Überraschung, 
  die sowohl &lt;hl>im Sommer wie auch zu Silvester&lt;hl> funktioniert.
  
  #### Expected Question
  Zu welchen Gelegenheiten passt der Monk Sour gut?
  
  ## Model description
  
  The model is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad), which was pre-trained on [GermanQUAD](https://www.deepset.ai/germanquad). We further pre-trained it on questions annotated on drink receipts from [Mixology](https://mixology.eu/) ("drink600"). 
  We have not yet open sourced the dataset, since we do not own copyright on the source material.
  
  ## Training and evaluation data
  
  The training script can be accessed [here](https://github.com/d-e-h-i-o/german-qg).
  
  ## Evaluation
  
  It achieves a **BLEU-4 score of 29.80** on the drink600 test set (n=120) and **11.30** on the GermanQUAD test set. 
  Thus, fine-tuning on drink600 did not affect performance on GermanQuAD.
  
  In comparison, *german-qg-t5-quad* achieves a BLEU-4 score of **10.76** on the drink600 test set.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 100
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Framework versions
  
  - Transformers 4.13.0.dev0
  - Pytorch 1.10.0+cu102
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  