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-large-germanquad   
  **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-large 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 = 6536 answers, because we removed 76 wrong answers.
  
  See https://deepset.ai/germanquad for more details and dataset download in SQuAD format.
  
  ## Hyperparameters
  ```
  batch_size = 24
  n_epochs = 2
  max_seq_len = 384
  learning_rate = 3e-5
  lr_schedule = LinearWarmup
  embeds_dropout_prob = 0.1
  ```
  ## 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](https://deepset.ai/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.
  ![performancetable](https://images.prismic.io/deepset/1c63afd8-40e6-4fd9-85c4-0dbb81996183_german-qa-vs-xlm-r.png) 
  
  ## Authors
   **Timo Möller:** timo.moeller@deepset.ai     
   **Julian Risch:** julian.risch@deepset.ai   
   **Malte Pietsch:** malte.pietsch@deepset.ai   
   
  ## About us
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           <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/>
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           <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/>
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  [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
  
  
  Some of our other work: 
  - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
  - [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)
  
  ## Get in touch and join the Haystack community
  
  <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>. 
  
  We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">Discord community open to everyone!</a></strong></p>
  
  [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](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) 
  