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--- |
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language: |
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- en |
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tags: |
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- charboundary |
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- sentence-boundary-detection |
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- paragraph-detection |
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- legal-text |
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- legal-nlp |
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- text-segmentation |
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- cpu |
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- document-processing |
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- rag |
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license: mit |
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library_name: charboundary |
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pipeline_tag: text-classification |
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datasets: |
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- alea-institute/alea-legal-benchmark-sentence-paragraph-boundaries |
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- alea-institute/kl3m-data-snapshot-20250324 |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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- throughput |
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papers: |
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- https://arxiv.org/abs/2504.04131 |
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--- |
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# CharBoundary small Model |
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This is the small model for the [CharBoundary](https://github.com/alea-institute/charboundary) library (v0.5.0), |
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a fast character-based sentence and paragraph boundary detection system optimized for legal text. |
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## Model Details |
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- **Size**: small |
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- **Model Size**: 3.0 MB (SKOPS compressed) |
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- **Memory Usage**: 1026 MB at runtime |
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- **Training Data**: Legal text with ~50,000 samples from [KL3M dataset](https://huggingface.co/datasets/alea-institute/kl3m-data-snapshot-20250324) |
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- **Model Type**: Random Forest (32 trees, max depth 16) |
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- **Format**: scikit-learn model (serialized with skops) |
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- **Task**: Character-level boundary detection for text segmentation |
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- **License**: MIT |
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- **Throughput**: ~748K characters/second |
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## Usage |
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> **Important:** When loading models from Hugging Face Hub, you must set `trust_model=True` to allow loading custom class types. |
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> |
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> **Security Note:** The ONNX model variants are recommended in security-sensitive environments as they don't require bypassing skops security measures with `trust_model=True`. See the [ONNX versions](https://huggingface.co/alea-institute/charboundary-small-onnx) for a safer alternative. |
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```python |
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# pip install charboundary |
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from huggingface_hub import hf_hub_download |
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from charboundary import TextSegmenter |
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# Download the model |
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model_path = hf_hub_download(repo_id="alea-institute/charboundary-small", filename="model.pkl") |
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# Load the model (trust_model=True is required when loading from external sources) |
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segmenter = TextSegmenter.load(model_path, trust_model=True) |
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# Use the model |
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text = "This is a test sentence. Here's another one!" |
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sentences = segmenter.segment_to_sentences(text) |
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print(sentences) |
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# Output: ['This is a test sentence.', " Here's another one!"] |
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# Segment to spans |
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sentence_spans = segmenter.get_sentence_spans(text) |
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print(sentence_spans) |
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# Output: [(0, 24), (24, 44)] |
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``` |
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## Performance |
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The model uses a character-based random forest classifier with the following configuration: |
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- Window Size: 5 characters before, 3 characters after potential boundary |
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- Accuracy: 0.9970 |
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- F1 Score: 0.7730 |
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- Precision: 0.7460 |
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- Recall: 0.9870 |
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### Dataset-specific Performance |
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| Dataset | Precision | F1 | Recall | |
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|---------|-----------|-------|--------| |
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| ALEA SBD Benchmark | 0.624 | 0.718 | 0.845 | |
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| SCOTUS | 0.926 | 0.773 | 0.664 | |
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| Cyber Crime | 0.939 | 0.837 | 0.755 | |
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| BVA | 0.937 | 0.870 | 0.812 | |
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| Intellectual Property | 0.927 | 0.883 | 0.843 | |
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## Available Models |
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CharBoundary comes in three sizes, balancing accuracy and efficiency: |
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| Model | Format | Size (MB) | Memory (MB) | Throughput (chars/sec) | F1 Score | |
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|-------|--------|-----------|-------------|------------------------|----------| |
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| Small | [SKOPS](https://huggingface.co/alea-institute/charboundary-small) / [ONNX](https://huggingface.co/alea-institute/charboundary-small-onnx) | 3.0 / 0.5 | 1,026 | ~748K | 0.773 | |
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| Medium | [SKOPS](https://huggingface.co/alea-institute/charboundary-medium) / [ONNX](https://huggingface.co/alea-institute/charboundary-medium-onnx) | 13.0 / 2.6 | 1,897 | ~587K | 0.779 | |
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| Large | [SKOPS](https://huggingface.co/alea-institute/charboundary-large) / [ONNX](https://huggingface.co/alea-institute/charboundary-large-onnx) | 60.0 / 13.0 | 5,734 | ~518K | 0.782 | |
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## Paper and Citation |
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This model is part of the research presented in the following paper: |
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``` |
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@article{bommarito2025precise, |
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title={Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary}, |
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author={Bommarito, Michael J and Katz, Daniel Martin and Bommarito, Jillian}, |
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journal={arXiv preprint arXiv:2504.04131}, |
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year={2025} |
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} |
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``` |
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For more details on the model architecture, training, and evaluation, please see: |
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- [Paper: "Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary"](https://arxiv.org/abs/2504.04131) |
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- [CharBoundary GitHub repository](https://github.com/alea-institute/charboundary) |
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- [Annotated training data](https://huggingface.co/datasets/alea-institute/alea-legal-benchmark-sentence-paragraph-boundaries) |
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## Contact |
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This model is developed and maintained by the [ALEA Institute](https://aleainstitute.ai). |
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For technical support, collaboration opportunities, or general inquiries: |
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- GitHub: https://github.com/alea-institute/kl3m-model-research |
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- Email: hello@aleainstitute.ai |
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- Website: https://aleainstitute.ai |
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For any questions, please contact [ALEA Institute](https://aleainstitute.ai) at [hello@aleainstitute.ai](mailto:hello@aleainstitute.ai) or |
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create an issue on this repository or [GitHub](https://github.com/alea-institute/kl3m-model-research). |
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