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README.md
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We further evaluated the retrieval performance of the trained model on the full German Wikipedia with the GermanDPR test set as labels. To this end, we converted the GermanDPR test set to SQuAD format. The DPR model drastically outperforms the BM25 baseline with regard to recall@k.
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![performancetable](https://lh3.google.com/u/0/d/1lX6G0cp4NTx1yUWs74LI0Gcs41sYy_Fb=w2880-h1578-iv1)
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## Usage
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### In haystack
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You can load the model in [haystack](https://github.com/deepset-ai/haystack/) as a retriever for doing QA at scale:
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```python
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retriever = DensePassageRetriever(
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document_store=document_store,
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query_embedding_model="deepset/gbert-base-germandpr-question_encoder"
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passage_embedding_model="deepset/gbert-base-germandpr-ctx_encoder"
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)
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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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## About us
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![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
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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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By the way: [we're hiring!](http://www.deepset.ai/jobs)
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We further evaluated the retrieval performance of the trained model on the full German Wikipedia with the GermanDPR test set as labels. To this end, we converted the GermanDPR test set to SQuAD format. The DPR model drastically outperforms the BM25 baseline with regard to recall@k.
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![performancetable](https://lh3.google.com/u/0/d/1lX6G0cp4NTx1yUWs74LI0Gcs41sYy_Fb=w2880-h1578-iv1)
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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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## About us
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<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
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</div>
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<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
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<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
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</div>
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</div>
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[deepset](http://deepset.ai/) is the company behind the production-ready open-source AI framework [Haystack](https://haystack.deepset.ai/).
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Some of our other work:
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- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")](https://huggingface.co/deepset/tinyroberta-squad2)
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- [German BERT](https://deepset.ai/german-bert), [GermanQuAD and GermanDPR](https://deepset.ai/germanquad), [German embedding model](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1)
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- [deepset Cloud](https://www.deepset.ai/deepset-cloud-product), [deepset Studio](https://www.deepset.ai/deepset-studio)
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## Get in touch and join the Haystack community
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<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://docs.haystack.deepset.ai">Documentation</a></strong>.
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We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>
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[Twitter](https://twitter.com/Haystack_AI) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://haystack.deepset.ai/) | [YouTube](https://www.youtube.com/@deepset_ai)
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By the way: [we're hiring!](http://www.deepset.ai/jobs)
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