Awesome

Use GPT-3 to solve chemistry problems. Most of the repo is currently not intended for use as library but as documentation of our experiments. We'll factor out the experiments (that come with tricky dependencies) into its own repository over time.

💪 Getting Started

from gptchem.gpt_classifier import GPTClassifier 
from gptchem.tuner import Tuner 

classifier = GPTClassifier(
    property_name="transition wavelength", # this is the property name we will use in the prompt template
    tuner=Tuner(n_epochs=8, learning_rate_multiplier=0.02, wandb_sync=False),
)

classifier.fit(["CC", "CDDFSS"], [0, 1])
predictions = classifier.predict(['CCCC', 'CCCCCCCC'])

The time these call take can vary as the methods call the OpenAI API under the hood. Therefore, in situation of high load, we also experienced hours of waiting time in the queue.

🚀 Installation

The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/kjappelbaum/gptchem.git

The installation should only take a few seconds to minutes. You can install additional depenencies using the extras experiments and eval.

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

📖 Citation

If you found this package useful, please cite our preprint

@inproceedings{Jablonka_2023,
	doi = {10.26434/chemrxiv-2023-fw8n4},
	url = {https://doi.org/10.26434%2Fchemrxiv-2023-fw8n4},
	year = 2023,
	month = {feb},
	booktitle = {ChemRxiv},
	author = {Kevin Maik Jablonka and Philippe Schwaller and Andres Ortega-Guerrero and Berend Smit},
	title = {Is {GPT} all you need for low-data discovery in chemistry?}
}

🛠️ For Developers

The final section of the README is for if you want to get involved by making a code contribution.

Development Installation

To install in development mode, use the following:

$ git clone git+https://github.com/kjappelbaum/gptchem.git
$ cd gptchem
$ pip install -e .

🥼 Testing

After cloning the repository and installing tox with pip install tox, the unit tests in the tests/ folder can be run reproducibly with:

$ tox

Additionally, these tests are automatically re-run with each commit in a GitHub Action.

📖 Building the Documentation

The documentation can be built locally using the following:

$ git clone git+https://github.com/kjappelbaum/gptchem.git
$ cd gptchem
$ tox -e docs
$ open docs/build/html/index.html

The documentation automatically installs the package as well as the docs extra specified in the setup.cfg. sphinx plugins like texext can be added there. Additionally, they need to be added to the extensions list in docs/source/conf.py.

📦 Making a Release

After installing the package in development mode and installing tox with pip install tox, the commands for making a new release are contained within the finish environment in tox.ini. Run the following from the shell:

$ tox -e finish

This script does the following:

1. Uses Bump2Version to switch the version number in the setup.cfg, src/gptchem/version.py, and docs/source/conf.py to not have the -dev suffix
2. Packages the code in both a tar archive and a wheel using build
3. Uploads to PyPI using twine. Be sure to have a .pypirc file configured to avoid the need for manual input at this step
4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use tox -e bumpversion minor after.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.