CharBoundary medium (default) ONNX Model
This is the medium (default) ONNX model for the CharBoundary library (v0.5.0), a fast character-based sentence and paragraph boundary detection system optimized for legal text.
Model Details
- Size: medium (default)
- Model Size: 2.6 MB (ONNX compressed)
- Memory Usage: 1897 MB at runtime (non-ONNX version)
- Training Data: Legal text with ~500,000 samples from KL3M dataset
- Model Type: Random Forest (64 trees, max depth 20) converted to ONNX
- Format: ONNX optimized for inference
- Task: Character-level boundary detection for text segmentation
- License: MIT
- Throughput: ~587K characters/second (base model; ONNX is typically 2-4x faster)
Usage
Security Advantage: This ONNX model format provides enhanced security compared to SKOPS models, as it doesn't require bypassing security measures with
trust_model=True. ONNX models are the recommended option for security-sensitive environments.
# Make sure to install with the onnx extra to get ONNX runtime support
# pip install charboundary[onnx]
from charboundary import get_medium (default)_onnx_segmenter
# First load can be slow
segmenter = get_medium (default)_onnx_segmenter()
# Use the model
text = "This is a test sentence. Here's another one!"
sentences = segmenter.segment_to_sentences(text)
print(sentences)
# Output: ['This is a test sentence.', " Here's another one!"]
# Segment to spans
sentence_spans = segmenter.get_sentence_spans(text)
print(sentence_spans)
# Output: [(0, 24), (24, 44)]
Performance
ONNX models provide significantly faster inference compared to the standard scikit-learn models while maintaining the same accuracy metrics. The performance differences between model sizes are shown below.
Base Model Performance
| Dataset | Precision | F1 | Recall |
|---|---|---|---|
| ALEA SBD Benchmark | 0.631 | 0.722 | 0.842 |
| SCOTUS | 0.938 | 0.775 | 0.661 |
| Cyber Crime | 0.961 | 0.853 | 0.767 |
| BVA | 0.957 | 0.875 | 0.806 |
| Intellectual Property | 0.948 | 0.889 | 0.837 |
Size and Speed Comparison
| Model | Format | Size (MB) | Memory Usage | Throughput (chars/sec) | F1 Score |
|---|---|---|---|---|---|
| Small | SKOPS / ONNX | 3.0 / 0.5 | 1,026 MB | ~748K | 0.773 |
| Medium | SKOPS / ONNX | 13.0 / 2.6 | 1,897 MB | ~587K | 0.779 |
| Large | SKOPS / ONNX | 60.0 / 13.0 | 5,734 MB | ~518K | 0.782 |
Paper and Citation
This model is part of the research presented in the following paper:
@article{bommarito2025precise,
title={Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary},
author={Bommarito, Michael J and Katz, Daniel Martin and Bommarito, Jillian},
journal={arXiv preprint arXiv:2504.04131},
year={2025}
}
For more details on the model architecture, training, and evaluation, please see:
- Paper: "Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary"
- CharBoundary GitHub repository
- Annotated training data
Contact
This model is developed and maintained by the ALEA Institute.
For technical support, collaboration opportunities, or general inquiries:
- GitHub: https://github.com/alea-institute/kl3m-model-research
- Email: [email protected]
- Website: https://aleainstitute.ai
For any questions, please contact ALEA Institute at [email protected] or create an issue on this repository or GitHub.
