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README.md
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language: de
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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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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
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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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language: de
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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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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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