distiluse-base-multilingual-cased has one more dense layer compared to the pool-only model. How is this dense layer added?

Hi, thank you for your great work.
distiluse-base-multilingual-cased has one more dense layer compared to the pool-only model. How is this dense layer added?
We are constructing a Chinese long text processing system based on the sentence-transformer. Among them, I used the published distilluse-base-multilingual-cased model. This model has a strong ability to express Chinese long text (<510 words), but no training code (in the example directory) for this model was found. A few days ago you posted training code for a new multilingual model. We trained for 20 rounds using the same format of Chinese corpus (TED2013-zh-zh, xnli-zh-zh, sts2017-zh-zh-by-google-translate). Only reached 70% accuracy of our dataset, and 0.748 cosine-pearson in evaluation step. And xlm-roberta consumes more capacity than distill-bert.

The issue mentions the published distiluse-base-multilingual-cased model and the example directory, but names no source file or test. Start by locating the model configuration and training entry point for that model; done would require explaining how the extra dense layer is added and addressing the requested training guidance.

Written by the indexing model from the issue text.