Given the task definition and input, reply with output. 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.

For model distillation BIBREF6 , we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0

where INLINEFORM0 is the cross-entropy function, INLINEFORM1 is the softmax function, INLINEFORM2 is the BERT model's logit of the current wordpiece, INLINEFORM3 is the small BERT model's logits and INLINEFORM4 is a temperature hyperparameter, explained in Section SECREF11 .

To train the distilled multilingual model mMiniBERT, we first use the distillation loss above to train the student from scratch using the teacher's logits on unlabeled data. Afterwards, we finetune the student model on the labeled data the teacher is trained on.
How do they compress the model?