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.

Example Input: Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts. 
 Question: What data is the language model pretrained on?
Example Output: Chinese general corpus

Example Input:  To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.  
 Question: What was their system's performance?
Example Output: accuracy and F1-score of 89.6% and 89.2%, respectively

Example Input: In contrast, our models are trained only on the 76K questions in the training set. 
 Question: Do the authors also try the model on other datasets?
Example Output:
No