| | --- |
| | 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 |
| | model-index: |
| | - name: deepset/gelectra-base-germanquad |
| | results: |
| | - task: |
| | type: question-answering |
| | name: Question Answering |
| | dataset: |
| | name: deepset/germanquad |
| | type: deepset/germanquad |
| | config: plain_text |
| | split: test |
| | metrics: |
| | - name: Exact Match |
| | type: exact_match |
| | value: 61.1615 |
| | verified: true |
| | - name: F1 |
| | type: f1 |
| | value: 77.5023 |
| | verified: true |
| | --- |
| | |
| |  |
| |
|
| | ## Overview |
| | **Language model:** gelectra-base-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-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. |
| |
|
| | 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. |
| | 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 [at] deepset.ai` |
| | - Julian Risch: `julian.risch [at] deepset.ai` |
| | - Malte Pietsch: `malte.pietsch [at] deepset.ai` |
| | ## About us |
| |  |
| | 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) |
| |
|
