Renard (Relationship Extraction from NARrative Documents) is a library for creating and using custom character networks extraction pipelines. Renard can extract dynamic as well as static character networks.
You can install the latest version using pip:
pip install renard-pipeline
Currently, Renard supports Python>=3.9,<=3.12
Documentation, including installation instructions, can be found at https://compnet.github.io/Renard/
If you need local documentation, it can be generated using Sphinx
. From the docs
directory, make html
should create documentation under docs/_build/html
.
You can check the interactive demo of Renard at HuggingFace. The UI used for the demo is currently in development and will be available directly in Renard in the next version.
Renard's central concept is the Pipeline
.A Pipeline
is a list of PipelineStep
that are run sequentially in order to extract a character graph from a document. Here is a simple example:
from renard.pipeline import Pipeline
from renard.pipeline.tokenization import NLTKTokenizer
from renard.pipeline.ner import NLTKNamedEntityRecognizer
from renard.pipeline.character_unification import GraphRulesCharacterUnifier
from renard.pipeline.graph_extraction import CoOccurrencesGraphExtractor
with open("./my_doc.txt") as f:
text = f.read()
pipeline = Pipeline(
[
NLTKTokenizer(),
NLTKNamedEntityRecognizer(),
GraphRulesCharacterUnifier(min_appearance=10),
CoOccurrencesGraphExtractor(co_occurrences_dist=25)
]
)
out = pipeline(text)
For more information, see renard_tutorial.py
, which is a tutorial in the jupytext
format. You can open it as a notebook in Jupyter Notebook (or export it as a notebook with jupytext --to ipynb renard-tutorial.py
).
see the "Contributing" section of the documentation.
Renard
uses pytest
for testing. To launch tests, use the following command :
uv run python -m pytest tests
Expensive tests are disabled by default. These can be run by setting the environment variable RENARD_TEST_ALL
to 1
.
If you use Renard in your research project, please cite it as follows:
@Article{Amalvy2024,
doi = {10.21105/joss.06574},
year = {2024},
publisher = {The Open Journal},
volume = {9},
number = {98},
pages = {6574},
author = {Amalvy, A. and Labatut, V. and Dufour, R.},
title = {Renard: A Modular Pipeline for Extracting Character
Networks from Narrative Texts},
journal = {Journal of Open Source Software},
}
We would be happy to hear about your usage of Renard, so don't hesitate to reach out!