readme: ---
  language: de
  datasets:
  - deepset/germanquad
  license: mit
  thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
  tags:
  - exbert
  ---
  
  ![bert_image](https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg)
  
  ## Overview
  **Language model:** gelectra-base-germanquad-distilled   
  **Language:** German  
  **Training data:** GermanQuAD train set (~ 12MB)  
  **Eval data:** GermanQuAD test set (~ 5MB)   
  **Infrastructure**: 1x V100 GPU  
  **Published**: Apr 21st, 2021
  
  ## Details
  - We trained a German question answering model with a gelectra-base model as its basis.
  - The dataset is GermanQuAD, a new, German language dataset, which we hand-annotated and published [online](https://deepset.ai/germanquad).
  - The training dataset is one-way annotated and contains 11518 questions and 11518 answers, while the test dataset is three-way annotated so that there are 2204 questions and with 2204·3−76 = 6536answers, because we removed 76 wrong answers.
  - In addition to the annotations in GermanQuAD, haystack's distillation feature was used for training. deepset/gelectra-large-germanquad was used as the teacher model.
  
  See https://deepset.ai/germanquad for more details and dataset download in SQuAD format.
  
  ## Hyperparameters
  ```
  batch_size = 24
  n_epochs = 6
  max_seq_len = 384
  learning_rate = 3e-5
  lr_schedule = LinearWarmup
  embeds_dropout_prob = 0.1
  temperature = 2
  distillation_loss_weight = 0.75
  ```
  ## Performance
  We evaluated the extractive question answering performance on our GermanQuAD test set.
  Model types and training data are included in the model name. 
  For finetuning XLM-Roberta, we use the English SQuAD v2.0 dataset.
  The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned on \\\\germanquad. 
  The human baseline was computed for the 3-way test set by taking one answer as prediction and the other two as ground truth.
  ```
  "exact": 62.4773139745916
  "f1": 80.9488017070188
  ```
  ![performancetable](https://lh3.google.com/u/0/d/1IFqkq8OZ7TFnGzxmW6eoxXSYa12f2M7O=w1970-h1546-iv1) 
  
  ## Authors
  - Timo Möller: `timo.moeller [at] deepset.ai`
  - Julian Risch: `julian.risch [at] deepset.ai`
  - Malte Pietsch: `malte.pietsch [at] deepset.ai`
  - Michel Bartels: `michel.bartels [at] deepset.ai`
  ## About us
  ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
  We bring NLP to the industry via open source!  
  Our focus: Industry specific language models & large scale QA systems.  
    
  Some of our work: 
  - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
  - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
  - [FARM](https://github.com/deepset-ai/FARM)
  - [Haystack](https://github.com/deepset-ai/haystack/)
  
  Get in touch:
  [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
  
  By the way: [we're hiring!](http://www.deepset.ai/jobs)