| | --- |
| | 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 |
| | --- |
| | |
| |  |
| |
|
| | ## 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. |
| |  |
| |
|
| | ## Authors |
| | **Timo Möller:** timo.moeller@deepset.ai |
| | **Julian Risch:** julian.risch@deepset.ai |
| | **Malte Pietsch:** malte.pietsch@deepset.ai |
| | |
| | ## About us |
| | <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3"> |
| | <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
| | <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/> |
| | </div> |
| | <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center"> |
| | <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/> |
| | </div> |
| | </div> |
| | |
| | [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. |
| |
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| |
|
| | 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) |
| |
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