A NLP library for data augmentation focusing on spatial information contained in text.
Project description
GeoNLPlify :earth_africa: :book:
A NLP library for data augmentation focusing on spatial information contained in text.
Usage
import geonlplify
my_text = "My name is Clara and I live in Berkeley."
geonlplify.geonlplify(my_text)
'My name is Clara and I live in Bristol'
Installation
You can install GeoNLPlify in three ways: in a virtual_env, in a conda environment, or using :construction: pip
Virtual Environment
- Git clone this repository
git clone https://github.com/remydecoupes/GeoNLPlify.git
- Create a virtual env
python -m venv geonlplify_venv
source geonlplify_venv/bin/activate
pip install --upgrade pip
- Install dependencies
cd GeoNLPlify
pip install -r virtual_env_requirements.txt
- Donwload world-cities from simple maps
wget -qO- https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.75.zip | bsdtar -xvf- -C ./geonlplify/simplemaps/
Conda environment
- Git clone this repository
git clone https://github.com/remydecoupes/GeoNLPlify.git
- Create a conda with all the required dependencies
cd GeoNLPlify
conda env create -n geonlplify_conda --file conda_environment.yml python==3.10.6
conda activate geonlplify_conda
- Install spacy models
python -m spacy download en_core_web_trf
- Donwload world-cities from simple maps
wget -qO- https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.75.zip | bsdtar -xvf- -C ./geonlplify/simplemaps/
Pip install
:construction: Packaging in progress :construction:
Acknowledgement
:pray: This library use those terrific tools/libraries/data :muscle::
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
GeoNLPlify-0.1.0.tar.gz
(18.0 kB
view hashes)
Built Distribution
GeoNLPlify-0.1.0-py3-none-any.whl
(17.7 kB
view hashes)
Close
Hashes for GeoNLPlify-0.1.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 637ce9e8c04dd653c672cee5ec569524e685650d0fdfb34bf26c710e5f48559e |
|
MD5 | fda879572784573a0b1115c01ab27099 |
|
BLAKE2b-256 | 39f28c777999a55733ce819a83e2c403ce1318d8e9e26f6d047cd41d0128ffe8 |