In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
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Question: The functional dissimilarity score was computed using sentences from the test set in CoNLL 2017 Universal Dependencies task BIBREF20 for the relevant languages with the provided UPOS sequences. We computed the nearest neighbours experiment for all languages in the training data for the above models. 
 Question: Which evaluation metrics do they use for language modelling?

Answer:  functional dissimilarity score nearest neighbours experiment


Question: We combine 13k questions gathered from this pipeline with an additional 3k questions with yes/no answers from the NQ training set to reach a total of 16k questions.  
 Question: what is the size of BoolQ dataset?

Answer:  16k questions


Question: The reports are published by Météo France and the Met Office, its British counterpart. They are publicly available on the respective websites of the organizations. Both corpora span on the same period as the corresponding time series and given their daily nature, it yields a total of 4,261 and 4,748 documents respectively. 
 Question: How big is dataset used for training/testing?

Answer:
4,261  days for France and 4,748 for the UK