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.

[Q]: To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements.
Question: How big is dataset used for fine-tuning BERT?
[A]: hundreds of thousands of legal agreements

[Q]: Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed.
Question: Which matrix factorization methods do they use?
[A]: weighted factorization of a word-context co-occurrence matrix

[Q]: We use the pre-trained uncased BERT$_\mathrm {BASE}$ model for fine-tuning, because we find that BERT$_\mathrm {LARGE}$ model performs slightly worse than BERT$_\mathrm {BASE}$ in this task.
Question: Do they use large or small BERT?
[A]:
small BERT