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https://github.com/usnistgov/chemnlp
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
https://github.com/usnistgov/chemnlp
ai llm natural-language-processing nist-jarvis python transformers
Last synced: 16 days ago
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ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
- Host: GitHub
- URL: https://github.com/usnistgov/chemnlp
- Owner: usnistgov
- License: mit
- Created: 2022-08-10T11:43:44.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-19T19:40:05.000Z (5 months ago)
- Last Synced: 2024-11-06T22:58:09.063Z (3 months ago)
- Topics: ai, llm, natural-language-processing, nist-jarvis, python, transformers
- Language: Python
- Homepage: https://doi.org/10.1021/acs.jpcc.3c03106
- Size: 1.85 MB
- Stars: 73
- Watchers: 9
- Forks: 16
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_Example.ipynb)
[](https://zenodo.org/badge/latestdoi/523320947)
# ChemNLP
# Table of Contents
* [Introduction](#intro)
* [Installation](#install)
* [Examples](#example)
* [Web-app](#webapp)
* [Reference](#reference)
Introduction
-------------------------
ChemNLP is a software-package to process chemical information from the scientific literature.
Installation
-------------------------
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
```
#### Method 1 (using setup.py):
Now, let's install the package:
```
git clone https://github.com/usnistgov/chemnlp.git
cd chemnlp
python setup.py develop
cde data download
```
#### Method 2 (using pypi):
As an alternate method, ChemNLP can also be installed using `pip` command as follows:
```
pip install chemnlp
cde data download
```
Examples
---------
#### Parse chemical formula
```
run_chemnlp.py --file_path="chemnlp/tests/XYZ"
```
#### Text classification example
```
python chemnlp/classification/scikit_class.py --csv_path chemnlp/sample_data/cond_mat_small.csv
```
[Google Colab example for installation and text classification](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_Example.ipynb)
#### Text generation example
[Google Colab example for Text Generation with HuggingFace](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_TitleToAbstract.ipynb)
Using the webapp
---------
The webapp is available at: https://jarvis.nist.gov/jarvischemnlp
1. [ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data](https://github.com/usnistgov/chemnlp)
2. [AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design](https://pubs.acs.org/doi/full/10.1021/acs.jpclett.4c01126)
3. [JARVIS-Leaderboard](https://pages.nist.gov/jarvis_leaderboard)
4. [NIST-JARVIS Infrastructure](https://jarvis.nist.gov/)