The current text splitting approaches rely on heuristics (although one can use neural embedder to group semantically related sentences).
I propose a fully neural approach to semantic chunking.
I took the base distilbert model and trained it on a bookcorpus to split concatenated text paragraphs into original paragraphs. Basically it’s a token classification task. Model fine-tuning took day and a half on a 2x1080ti.
The library could be used as a text splitter module in a RAG system or for splitting transcripts for example.
The usage pattern that I see is the following: strip all the markup tags to produce pure text and feed this text into the model.
The problem is that although in theory this should improve overall RAG pipeline performance I didn’t manage to measure it properly. Other limitations: the model only supports English for now and the output text is downcased.
Please give it a try. I'll appreciate a feedback.
The Python library: https://github.com/mirth/chonky
The transformer model: https://huggingface.co/mirth/chonky_distilbert_base_uncased_...
It uses a similar approach but the focus is on sentence/paragraph segmentation generally and not specifically focused on RAG. It also has some benchmarks. Might be a good source of inspiration for where to take chonky next.