The ChemNLP project aims to

1. create an extensive chemistry dataset and
2. use it to train large language models (LLMs) that can leverage the data for a wide range of chemistry applications.

For more details see our information material section below.

• Introduction presentation
• Project proposal
• Task board
• awesome-chemistry-datasets repository to collect interesting chemistry datasets
• Weekly meetings are set up soon! Please join our Discord community for more information.

Feel free to join our #chemnlp channel on our OpenBioML discord server to start the discussion in more detail.

ChemNLP is an open-source project - your involvement is warmly welcome! If you're excited to join us, we recommend the following steps:

• Join our Discord server.
• Have a look at our contributing guide.
• Looking for ideas? See our task board to see what we may need help with.
• Have an idea? Create an issue!

Our OpenBioML ChemNLP project is not afiliated to the ChemNLP library from NIST and we use "ChemNLP" as a general term to highlight our project focus. The datasets and models we create through our project will have a unique and recognizable name when we release them.

See https://openbioml.org, especially our approach and partners.

Create a new conda environment with Python 3.8:

conda create -n chemnlp python=3.8
conda activate chemnlp

To install the chemnlp package (and required dependencies):

pip install chemnlp

If working on developing the python package:

pip install -e "chemnlp[dev]"  # to install development dependencies

If extra dependencies are required (e.g. for dataset creation) but are not needed for the main package please add to the pyproject.toml in the dataset_creation variable and ensure this is reflected in the conda.yml file.

Note

If working on model training, request access to the wandb project chemnlp and log-in to wandb with your API key per here.

We specify datasets by creating a new function here which is named per the dataset on Hugging Face. At present the function must accept a tokenizer and return back the tokenized train and validation datasets.