LexNLP is a library for working with real, unstructured legal text, including contracts, plans, policies, procedures, and other material.
- Segmentation and tokenization, such as
- A sentence parser that is aware of common legal abbreviations like LLC. or F.3d.
- Pre-trained segmentation models for legal concepts such as pages or sections.
- Pre-trained word embedding and topic models, broadly and for specific practice areas
- Pre-trained classifiers for document type and clause type
- Broad range of fact extraction, such as:
- Monetary amounts, non-monetary amounts, percentages, ratios
- Conditional statements and constraints, like "less than" or "later than"
- Dates, recurring dates, and durations
- Courts, regulations, and citations
- Tools for building new clustering and classification methods
- Hundreds of unit tests from real legal documents
- ContraxSuite: https://contraxsuite.com/
- LexPredict: https://lexpredict.com/
- Official Website: https://lexnlp.com/
- Documentation: http://lexpredict-lexnlp.readthedocs.io/en/latest/ (in progress)
- Contact: [email protected]
LexNLP is available under a dual-licensing model. By default, this library can be used under AGPLv3 terms as detailed in the repository LICENSE file; however, organizations can request a release from the AGPL terms or a non-GPL evaluation license by contacting ContraxSuite Licensing at <[email protected]>.
- Python 3.12
- pipenv
- conda
Some of the required packages (especially those related to scikit-learn) will fail to build wheels when LexNLP is installed on a M1 Mac.
For the package to be installed successfully, you can either use pre-compiled packages, or install a version of scikit-learn from the conda-forge installation that contains a C/C++ compiler (you can use miniforge). You can find the pre-compiled packages in this repo.
As of October 23, 2024, scikit-learn has native support of ARM. However, the issue persisted until I created a conda environment with the steps below.
Furthermore, LexNLP's dependencies are strict and lead to failed installation. This repo contains modified versions of setup.py and python-requirements.txt that (hopefully) will address this problem. WARNING: I have only tested extensively scripts related to definitions. The new installation instructions may end up breaking other LexNLP functions.
Download this directory (don't install it yet). Move the directory to wherever you prefer. Create a new virtual environment w/ scikit-learn specified:
conda create -n myenv -c conda-forge scikit-learn
where myenv is your environment name.
Activate the environment and install pip into this environment:
conda activate myenv
conda install pip
Navigate to the downloaded source directory and run
pip install .
which executes the modified setup.py using the requirements we just copied over.
This should successfully install the package. You may have to downgrade setuptools to ~=70.0.0.