## What is it ?

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legal-doc-processing is an open source NLP library dedicated to legal documents. It offers a large and various tools to analyse, structure and extract information from legal documents surch as orders, complaints, press release etc etc. <br> <br>

## Installation

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go in your project directory and activate virtual environnement ` cd my-project/ python3 -m venv env source ./env/bin/activate `

then install with pypi ` pip install legal-doc-processing `

or install with git ` git clone https://github.com/THEOLEX-IO/legal_doc_processing.git pip install -r requirements.txt `

at frist usage please write following command to bootstrap the package ` python -c "from legal_doc_processing import boot; boot()" ` this command will download data collections and mandory web assets, it can take 1/2 minutes (depends of your web connection) <br> <br>

## Usage

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There is 2 main modules in legal-doc-processing : - ld for LegalDoc objects ie order, complaint, etc etc official documents - pr for PressRelease objects for legal press release related to each case

so you can ` from legal_doc_processing import * # import all from legal_doc_processing import ld # import legal document module from legal_doc_processing import pr # import press release module ` <br>

### Instanciation youn can init an object in 2 ways. * with text directly ` from legal_doc_processing import ld doc = ld.LegalDoc("this is a document") ` * with path to a file ` doc = ld.read_LegalDoc("this/is/my/file.txt") `

for press release, same pattern: ` from legal_doc_processing import pr press = ld.PressRelease("this is a press release") ` * with path to a file ` press = ld.read_PressRelease("this/is/my/file.txt") `

once instanciated, you can print : ` print(doc) ` <br>

all interessing features are in feature_dict attribute ` print(doc.feature_dict) ` <br>

### Predictions

you can now make predictions : ` defendant = doc.predict("defendant") print(defendant) case = doc.predict("case") print(case) ` <br>

predict method return a something but alson work on the object itself : ` print(doc) print(doc.feature_dict) ` <br>

of course most easy is to predict all : ` features = doc.predict("all") print(features) print(doc) print(doc.feature_dict) ` <br> <br>

## Requirements

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Package requirements are handled using pip. To install them do ` pip install -r requirements.txt ` <br> <br>

## Tests

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Testing is set up using [pytest](http://pytest.org) and coverage is handled with the pytest-cov plugin.

Run your tests with `pytest` in the root directory.

Coverage is ran by default and is set in the `pytest.ini` file. To see an html output of coverage open `htmlcov/index.html` after running the tests. <br> <br>

## Pipe steps:

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Cleaning and feature engineering –> segmentation –> classification –> information extraction <br> <br>

## Licence:

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lorem ipsum <br> <br>

## Getting Help and discussion:

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lorem ipsum <br> <br>

## Contributing:

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## Documentation:

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