• Introduction
• Installation
• Examples
• Web-app
• Reference

ChemNLP is a software-package to process chemical information from the scientific literature.

First create a conda environment: Install miniconda environment from https://conda.io/miniconda.html Based on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'.

Now,

bash Miniconda3-latest-Linux-x86_64.sh (for linux)
bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)

Download 32/64 bit python 3.8 miniconda exe and install (for windows) Now, let's make a conda environment, say "chemnlp", choose other name as you like::

conda create --name chemnlp python=3.9
source activate chemnlp

Now, let's install the package:

git clone https://github.com/usnistgov/chemnlp.git
cd chemnlp
python setup.py develop
cde data download

As an alternate method, ChemNLP can also be installed using pip command as follows:

pip install chemnlp
cde data download

run_chemnlp.py --file_path="chemnlp/tests/XYZ"

python chemnlp/classification/scikit_class.py --csv_path chemnlp/sample_data/cond_mat_small.csv

Google Colab example for installation and text classification

Google Colab example for Text Generation with HuggingFace

The webapp is available at: https://jarvis.nist.gov/jarvischemnlp

1. ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
2. AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design
3. JARVIS-Leaderboard
4. NIST-JARVIS Infrastructure

Note: This project was originally developed under the github.com/usnistgov organization. New updates and developments will be carried out here.