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Document chunking in
summarizer.py
was still failing with some models on some texts. (There were some model vocabularies that caused the model server to fail to return the detokenized data if specific token sequences were split into separate chunks by the chunking process).To address this issue, we've implemented a new approach to chunking. Instead of chunking the raw tokens, we will chunk the text, like in our earlier implementation, and rely on
extras/tokenize/count
to confirm that the text chunk is within the token limit, if not, we split the text chunk in two.I've manually evaluated this with the problematic and non-problemtatic models. It appears to work as well as before, and avoids any of the detokenization crashes we were experiencing.