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--- |
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language: en |
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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:** deepset/roberta-base-squad2-distilled |
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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/roberta-large-squad2 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 = 80 |
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n_epochs = 4 |
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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 = 1.5 |
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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 the SQuAD v2 dev 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": 79.8366040596311 |
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"f1": 83.916407079888 |
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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) |
