TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks.

SOLUTION: What hyperparameters have been tuned?

PROBLEM: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.

SOLUTION: How big is dataset used for fine-tuning BERT?

PROBLEM: We use the MedWeb (“Medical Natural Language Processing for Web Document”) dataset BIBREF4 that was provided as part of a subtask at the NTCIR-13 Conference BIBREF5. The data is summarised in Table TABREF1. There are a total of 2,560 pseudo-tweets in three different languages: Japanese (ja), English (en) and Chinese (zh).

SOLUTION:
How big is dataset used for fine-tuning model for detection of red flag medical symptoms in individual statements?