For your information, there are only 500 documents in Brown corpus, so if we were to use batches, we might probably end up with optimal batch size of 10 or so **for Brown corpus**. In the paper you linked in the planned feature, batch size should be small enough to prevent long running task reduce the parallelism effectiveness, but big enough to hide IPC overhead. Then I would say that it really depends on the input size. So your suggestion on automatic group size inference by analyzing the first 100 jobs is great.
Regardless whether that feature is implemented, I think it's good if we let the group size be customizable by user in `CountVectorizer`, which is something that I can do now. What do you think?

Also, to make sure I understand you correctly, when using batch mode, then we need to change `_count_vocab_process` to accept a list of documents instead of just a single document, right?