Chonky – a neural text semantic chunking goes multilingual
TLDR: I’m expanding the family of text-splitting Chonky models with new multilingual model. You can learn more about this neural approach in a previous post: https://news.ycombinator.com/item?id=43652968 Since the release of the first distilbert-based model I’ve released two more models based on a ModernBERT. All these models were pre-trained and fine-tuned primary on English texts. But recently mmBERT(https://huggingface.co/blog/mmbert) has been released. This model pre-trained on massive dataset that contains 1833 languages. So I had an idea of…
Chonky – a neural text semantic chunking goes multilingual launched on October 25, 2025 with 43 votes, #434 of 8,178 launches that month and more than 90% of that year's launches.
In plain words
Chonky is a neural text semantic chunking tool that splits documents into meaningful chunks for processing. It now includes multilingual models trained on 1,833 languages using mmBERT, expanding beyond its original English-focused versions. The models use neural approaches based on DistilBERT and ModernBERT architectures, trained on diverse datasets including books in multiple languages. It is designed for developers and teams working with text processing and natural language applications across different languages.
written from the facts on this page · September 2026
From the sources
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Chonky is a transformer model that intelligently segments text into meaningful semantic chunks. This model can be used in the RAG systems. The model processes text and divides it into semantically coherent segments. These chunks can then be fed into embedding-based retrieval systems or language models as part of a RAG pipeline. ⚠️This model was fine-tuned on sequence of length 1024 (by default mmBERT supports sequence length up to 8192). from src.chonky import ParagraphSplitter # on the first run it will download the transformer model splitter = ParagraphSplitter( model_id="mirth/chonky_mmbert_small_multilingual_1", device="cpu" ) text = ( "Before college the two main things I worked on,…from huggingface.co
In the maker’s words, at launch
TLDR: I’m expanding the family of text-splitting Chonky models with new multilingual model. You can learn more about this neural approach in a previous post: https://news.ycombinator.com/item?id=43652968 Since the release of the first distilbert-based model I’ve released two more models based on a…
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Chonky – a neural approach for text semantic chunking2025 · github.com · ▲169TLDR: I’ve made a transformer model and a wrapper library that segments text into meaningful semantic chunks. 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…
Chonkie – A Fast, Lightweight Text Chunking Library for RAG2024 · github.com · ▲199I built Chonkie because I was tired of rewriting chunking code for RAG applications. Existing libraries were either too bloated (80MB+) or too basic, with no middle ground. Core features: - 21MB default install vs 80-171MB alternatives - 33x faster token chunking than popular alternatives - Supports multiple chunking strategies: token, word, sentence, and semantic - Works with all major tokenizers (transformers, tokenizers, tiktoken) - Zero external dependencies for basic functionality Technical optimizations: - Uses tiktoken with multi-threading for faster tokenization - Implements…
I modeled the Voynich Manuscript with SBERT to test for structure2025 · github.com · ▲381I built this project as a way to learn more about NLP by applying it to something weird and unsolved. The Voynich Manuscript is a 15th-century book written in an unknown script. No one’s been able to translate it, and many think it’s a hoax, a cipher, or a constructed language. I wasn’t trying to decode it — I just wanted to see: does it behave like a structured language? I stripped a handful of common suffix-like endings (aiin, dy, etc.) to isolate what looked like root forms. I know that’s a strong assumption — I call it out directly in the repo — but it helped clarify the clustering. From…
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- ACAdvanced Chunking in JavaScript/TypeScript with Chonkie2025 · ▲10
Hi HN, We’re Shreyash and Bhavnick. We built Chonkie, an open-source library for advanced chunking and embedding of text and code. It was previously Python-only, but we just released a TypeScript version: https://github.com/chonkie-inc/chonkie-ts Many AI projects in JS/TS (like those using Vercel's AI SDK or Mastra) rely on basic text splitters. But better chunking = better retrieval = better performance. That’s what Chonkie is built for. Current native chunkers (in TS): - Code Chunker – handles Python, TypeScript, etc. - Recursive Chunker – rule-based, hierarchical…
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