gelectra large for Extractive QA Overview Language model: gelectra large germanquad Language: German Training data: GermanQuAD train set (~ 12MB) Eval data: GermanQuAD test set (~ 5MB) Code: See an example extractive QA pipeline built with Haystack 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. 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 Usage In Haystack Haystack is an AI orchestration framework to build customizable, production ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, ch…
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