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  # bert-chunker-2
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- bert-chunker-2 is a text chunker based on BERT with a classifier head to predict the start token of chunks (for use in RAG, etc), and using a sliding window it cuts documents of any size into chunks. bert-chunker-2 is a text chunker based on BertForTokenClassification to predict the start token of chunks (for use in RAG, etc), and using a sliding window it cuts documents of any size into chunks. We see it as an alternative of [semantic chunker](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb). Speciallly, it not only works for the structured texts, but also the **unstructured and messy texts**. As a new experimental version of [bert-chunker](https://huggingface.co/tim1900/bert-chunker), it's enhanced for article structures, aiming to reach a balance between semantic chunking and structure chunking. It is a 0.1:0.9 linear weight merging of a trained semantic chunker and a trained structure chunker.
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  **A new, more mature version is [bert-chunker-3](https://huggingface.co/tim1900/bert-chunker-3)**.
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  ---
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  # bert-chunker-2
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+ bert-chunker-2 is a text chunker based on BERT with a classifier head to predict the start token of chunks (for use in RAG, etc), and using a sliding window it cuts documents of any size into chunks. bert-chunker-2 is a text chunker based on BertForTokenClassification to predict the start token of chunks (for use in RAG, etc), and using a sliding window it cuts documents of any size into chunks. We see it as an alternative of [semantic chunker](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb), but speciallly, it not only works for the structured texts, but also the **unstructured and messy texts**. As a new experimental version of [bert-chunker](https://huggingface.co/tim1900/bert-chunker), it's enhanced for article structures, aiming to reach a balance between semantic chunking and structure chunking. It is a 0.1:0.9 linear weight merging of a trained semantic chunker and a trained structure chunker.
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  **A new, more mature version is [bert-chunker-3](https://huggingface.co/tim1900/bert-chunker-3)**.
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