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@@ -38,7 +38,7 @@ embeds_dropout_prob = 0.1
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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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  ![performancetable](https://images.prismic.io/deepset/1c63afd8-40e6-4fd9-85c4-0dbb81996183_german-qa-vs-xlm-r.png)
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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](https://deepset.ai/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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  ![performancetable](https://images.prismic.io/deepset/1c63afd8-40e6-4fd9-85c4-0dbb81996183_german-qa-vs-xlm-r.png)
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