granuscore 1.0.1

Granularity scoring for natural language

Granuscore

Granuscore is a Python library for measuring the semantic granularity of natural language text.

It provides an end-to-end pipeline that:

1. splits text into referential units,
2. assigns continuous granularity scores to each unit,
3. aggregates these scores into document-level estimates.

Granuscore is designed for analyzing how fine-grained or coarse-grained textual expressions are in applications such as question answering, educational dialogue, summarization, and scientific writing.

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Installation

Install from PyPI:

pip install granuscore

Or install the latest development version locally:

git clone https://github.com/lukasellinger/granuscore.git
cd granuscore
pip install -e .

Optional development dependencies:

pip install -e ".[dev]"

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Quick Start

from granuscore import GranuScore

scorer = GranuScore()

text = """
Tony Hawk was born in San Diego.
"""

score = scorer(text)

print(score)

By default, Granuscore returns percentile scores, where higher values correspond to coarser-grained expressions.

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Default Configuration

The default configuration reproduces the setup used in the paper.

scorer = GranuScore()

Equivalent to:

scorer = GranuScore(
    predictor_type="hit",
)

Default settings:

• predictor_type="hit"
• model_name="Hierarchy-Transformers/HiT-MiniLM-L12-WordNetNoun"
• search_method="random_anchors"
• random_anchors_k=999

Required artifacts such as:

• FAISS indices,
• anchor vectors,
• LightGBM models,
• and reference percentile distributions

are automatically downloaded and cached on first use.

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Important Compatibility Note

The default configuration works out of the box and is the recommended setup.

If you customize components such as:

• the embedding model,
• search method,
• FAISS index,
• anchor vectors,
• or LightGBM model,

you must ensure that all resources are compatible with each other.

For example, a LightGBM model trained using:

search_method="random_anchors"

should not be combined with:

search_method="nearest_neighbor"

Similarly, FAISS indices, anchor vectors, percentile reference distributions, and LightGBM models must originate from the same embedding space and training configuration.

Compatibility between custom resources is not validated automatically.

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Notebook Tutorial

An interactive introduction is available in:

notebooks/getting_started.ipynb

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Repository Structure

granuscore/
├── src/
│   └── granuscore/
│       ├── pipeline.py
│       ├── granularity_predictor.py
│       ├── claim_splitter.py
│       ├── bucket_output.py
│       ├── cache.py
│       └── artifacts.py
├── notebooks/
│   ├── build_granola_dataset.ipynb
│   └── getting_started.ipynb
├── training_scripts/
├── evaluation/
├── assets/
├── data/ (needs to be externally downloaded)
├── pyproject.toml
├── LICENSE
└── README.md

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Reproducing Paper Experiments

The datasets and precomputed resources required to reproduce the experiments from the paper are available here:

https://drive.google.com/drive/folders/1mJdUENOxHEiuYn-_f1KRQ1PZggXJDnb4?usp=sharing

Download the archive and extract it into the repository root:

unzip data.zip

This will create the expected directory structure used by the training and evaluation scripts.

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GRANOLA Dataset Construction

We use a processed version of the GRANOLA-EQ Dataset introduced by Yona et al. (2024). Due to licensing restrictions of upstream resources, the processed dataset version used in this work is not redistributed directly in this repository or on external hosting platforms.

Instead, the dataset can be reconstructed locally using the notebook:

notebooks/build_granola_dataset.ipynb

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Training Pipeline

Training uses precomputed .pkl feature files.

1. Generate precomputed datasets:

training_scripts/build_precalc_data/

2. Train LightGBM models:

python training_scripts/train_lgb_models.py

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Citation

@misc{ellinger2026granuscorereferencefreemeasuregranularity,
      title={Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering}, 
      author={Lukas Ellinger and Alexander Fichtl and Miriam Anschütz and Georg Groh},
      year={2026},
      eprint={2605.26620},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.26620}, 
}