🪓 wtpsplit is a Python package that offers training, inference, and evaluation of state-of-the-art Segment any Text (SaT) models for partitioning text into sentences.
✂️ wtpsplit-lite is a lightweight version of wtsplit that only retains accelerated ONNX inference of SaT models with minimal dependencies:
- huggingface-hub to download the model
- numpy to process the model in- and output
- onnxruntime to run the model
- tokenizers to tokenize the text for the model
To install this package, run:
pip install wtpsplit-lite
Tip
For a complete list of Segment any Text (SaT) models and all SaT.split
keyword arguments, see the wtsplit README.
Example usage:
from wtpsplit_lite import SaT
text = """
It is known that Maxwell’s electrodynamics—as usually understood at the
present time—when applied to moving bodies, leads to asymmetries which do
not appear to be inherent in the phenomena. Take, for example, the recipro-
cal electrodynamic action of a magnet and a conductor.
"""
# Fast (~150ms/page), good quality:
sat = SaT("sat-3l-sm")
sentences = sat.split(text, stride=128, block_size=256)
# Slow, highest quality:
sat = SaT("sat-12l-sm")
sentences = sat.split(text)
This package also contributes a new 'hat' weighting scheme to wtpsplit that improves output quality when using large strides. To enable it, set weighting="hat"
as follows:
# Fast (~150ms/page), better quality:
sat = SaT("sat-3l-sm")
sentences = sat.split(text, stride=128, block_size=256, weighting="hat")
Note
In wtpsplit, the SaT implementation treats newlines as sentence boundaries by default. However, this leads to poor results on text extracted from PDF such as in the example above. In wtpsplit-lite, newlines are therefore treated as whitepace by default. You can choose which behavior you prefer with the treat_newline_as_space
boolean keyword argument of the SaT.split
method.
Prerequisites
-
Generate an SSH key and add the SSH key to your GitHub account.
-
Configure SSH to automatically load your SSH keys:
cat << EOF >> ~/.ssh/config Host * AddKeysToAgent yes IgnoreUnknown UseKeychain UseKeychain yes ForwardAgent yes EOF
-
Install VS Code and VS Code's Dev Containers extension. Alternatively, install PyCharm.
-
Optional: install a Nerd Font such as FiraCode Nerd Font and configure VS Code or PyCharm to use it.
Development environments
The following development environments are supported:
-
⭐️ GitHub Codespaces: click on Open in GitHub Codespaces to start developing in your browser.
-
⭐️ VS Code Dev Container (with container volume): click on Open in Dev Containers to clone this repository in a container volume and create a Dev Container with VS Code.
-
⭐️ uv: clone this repository and run the following from root of the repository:
# Create and install a virtual environment uv sync --python 3.10 --all-extras # Activate the virtual environment source .venv/bin/activate # Install the pre-commit hooks pre-commit install --install-hooks
-
VS Code Dev Container: clone this repository, open it with VS Code, and run Ctrl/⌘ + ⇧ + P → Dev Containers: Reopen in Container.
-
PyCharm Dev Container: clone this repository, open it with PyCharm, create a Dev Container with Mount Sources, and configure an existing Python interpreter at
/opt/venv/bin/python
.
Developing
- This project follows the Conventional Commits standard to automate Semantic Versioning and Keep A Changelog with Commitizen.
- Run
poe
from within the development environment to print a list of Poe the Poet tasks available to run on this project. - Run
uv add {package}
from within the development environment to install a run time dependency and add it topyproject.toml
anduv.lock
. Add--dev
to install a development dependency. - Run
uv sync --upgrade
from within the development environment to upgrade all dependencies to the latest versions allowed bypyproject.toml
. Add--only-dev
to upgrade the development dependencies only. - Run
cz bump
to bump the package's version, update theCHANGELOG.md
, and create a git tag. Then push the changes and the git tag withgit push origin main --tags
.