| --- |
| language: |
| - pl |
| - en |
| license: apache-2.0 |
| base_model: answerdotai/ModernBERT-base |
| tags: |
| - chunking |
| - semantic-segmentation |
| - token-classification |
| - modernbert |
| - nlp |
| - rag |
| pipeline_tag: token-classification |
| datasets: |
| - wikimedia/wikipedia |
| --- |
| |
| # ModernBERT Chunker Base 🚀 |
|
|
| This model is a fine-tuned version of **ModernBERT-base**, specialized in **semantic boundary detection**. It is designed to be used with the [fine-chunker](https://github.com/JerzyCode/fine-chunker) library for high-quality text segmentation in RAG applications. |
|
|
| ## Model Highlights |
| - **Context Length**: 8192 tokens (full ModernBERT capacity). |
| - **Architecture**: ModernBERT-base + Deep Classification Head (Linear-ReLU-Dropout-Linear). |
| - **Training Strategy**: Sequential packing of full Wikipedia articles with weighted Cross-Entropy. |
| - **Languages**: Bilingual support for **Polish** and **English**. |
|
|
| ## Usage |
|
|
| The easiest way to use this model is through the official library: |
|
|
| ```python |
| from fine_chunker import Chunker |
| |
| # Load the model (runs optimally on CUDA or CPU) |
| chunker = Chunker.from_pretrained(device="cpu", use_onnx=True) |
| |
| text = "Your long multi-topic document..." |
| chunks = chunker.chunk(text) |
| |
| for chunk in chunks: |
| print(f"Index: {chunk.index} | Content: {chunk.content[:100]}...") |
| ``` |
|
|
| ## Training Details |
|
|
| ### Dataset |
| The model was trained on **Wikipedia (20231101 version)** for both Polish and English. |
| - **Preprocessing**: Full articles were cleaned of wiki-noise (references, external links, metadata). Additionally, 40% of chunk starts were replaced by a lowercase letter, and 40% of the last dots in chunks were removed. |
| - **Ground Truth**: Segmentation was based on natural paragraph boundaries (`\n\n`) found in well-structured Wikipedia articles. |
| - **Packing**: Multiple articles were packed into single `8192` token sequences to maximize training efficiency. |
|
|
| ### Training Configuration |
| - **Hardware**: 4x NVIDIA A100-SXM4-40GB. |
| - **Duration**: 1 day, 6 hours, 1 minute. |
| - **Precision**: `bfloat16` with Flash Attention 2. |
| - **Epochs**: 1 |
| - **Optimization**: |
| - **Loss Function**: Weighted Cross-Entropy (`[1.0, 7.0]`) to address boundary sparsity. |
| - **Gradient Accumulation**: 8 steps. |
| - **Dropout**: 0.1. |
|
|
| ### Architecture Details |
| Unlike standard token classifiers that use a single linear layer, this model uses a **deep classification head**: |
| 1. `Linear(hidden_size, hidden_size)` |
| 2. `ReLU` |
| 3. `Dropout(0.1)` |
| 4. `Linear(hidden_size, 2)` (Boundary vs. Non-boundary) |
|
|
| This allows the model to learn more complex semantic cues for segmentation. |
|
|
| ## Intended Use |
| - **RAG Pipelines**: Generating semantic chunks that preserve context better than fixed-size splitting. |
| - **Long Document Analysis**: Segmenting reports, legal documents, or books into logical chapters/sections. |
| - **Pre-processing for LLMs**: Ensuring input fragments are semantically complete. |
|
|
| ## Limitations |
| - While effective on general knowledge, it may require further fine-tuning for extremely niche domains (e.g., medical or highly technical code documentation). |
| - Performance is best on texts with clear logical structures. |
|
|
| ## Evaluation |
| Status: Under Development > Systematic evaluation of the model's performance across different domains and languages is currently in progress. |
|
|
|
|
| ## Author |
| Developed by **Jerzy Boksa**. |
| Contact: devjerzy@gmail.com |
| GitHub: [fine-chunker](https://github.com/JerzyCode/fine-chunker) |
|
|
| ## Acknowledgements |
| This model was trained using the infrastructure provided by **Cyfronet** (Academic Computer Centre Cyfronet AGH) as part of a educational grant. |
|
|
| ## Citation |
|
|
| If you use this model or the `fine-chunker` library in your research or project, please cite it as follows: |
|
|
| ```bibtex |
| @misc{boksa2024modernbertchunker, |
| author = {Jerzy Boksa}, |
| title = {ModernBERT Chunker Base: Specialized Semantic Boundary Detection for RAG}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| journal = {Hugging Face Model Hub}, |
| howpublished = {\url{https://huggingface.co/jboksa/modbert-chunker-base}} |
| } |
