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README.md
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---
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language: de
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datasets:
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- deepset/germanquad
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license: mit
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thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
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tags:
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- exbert
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---
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## Overview
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**Language model:** gelectra-base-germanquad
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**Language:** German
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**Training data:** GermanQuAD train set (~ 12MB)
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**Eval data:** GermanQuAD test set (~ 5MB)
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**Infrastructure**: 1x V100 GPU
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**Published**: Apr 21st, 2021
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## Details
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- We trained a German question answering model with a gelectra-base model as its basis.
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- The dataset is GermanQuAD, a new, German language dataset, which we hand-annotated and published [online](https://deepset.ai/germanquad).
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- 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.
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- 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.
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See https://deepset.ai/germanquad for more details and dataset download in SQuAD format.
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## Hyperparameters
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```
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batch_size = 24
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n_epochs = 6
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max_seq_len = 384
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learning_rate = 3e-5
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lr_schedule = LinearWarmup
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embeds_dropout_prob = 0.1
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temperature = 2
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distillation_loss_weight = 0.75
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```
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## Performance
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We evaluated the extractive question answering performance on our GermanQuAD test set.
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Model types and training data are included in the model name.
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For finetuning XLM-Roberta, we use the English SQuAD v2.0 dataset.
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The GELECTRA models are warm started on the German translation of SQuAD v1.1 and finetuned on \\\\germanquad.
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The human baseline was computed for the 3-way test set by taking one answer as prediction and the other two as ground truth.
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```
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"exact": 62.4773139745916
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"f1": 80.9488017070188
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```
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## Authors
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- Timo Möller: `timo.moeller [at] deepset.ai`
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- Julian Risch: `julian.risch [at] deepset.ai`
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- Malte Pietsch: `malte.pietsch [at] deepset.ai`
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- Michel Bartels: `michel.bartels [at] deepset.ai`
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## About us
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We bring NLP to the industry via open source!
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Our focus: Industry specific language models & large scale QA systems.
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Some of our work:
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- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
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- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
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- [FARM](https://github.com/deepset-ai/FARM)
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- [Haystack](https://github.com/deepset-ai/haystack/)
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Get in touch:
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[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)
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By the way: [we're hiring!](http://www.deepset.ai/jobs)
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