We have several introductory tutorials to help get you started with using Emmental.
In this tutorial, we build an Emmental application to predicted 14 common diagnoses using convolutional neural networks in over 100,000 NIH chest x-rays proposed from the CheXNet paper.
In this tutorial, we build an Emmental application to tackle the General Language Understanding Evaluation (GLUE) benchmark which is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
In this tutorial, we build an Emmental application to tackle SuperGLUE, a new benchmark in the same style as GLUE with a set of more difficult language understanding tasks. Our submission achieved a new start-of-the-art score on June 15, 2019 under the name of Stanford Hazy Research. This code has also been refactored to be compatible with the Snorkel project in the snorkel-superglue repository.
For the Python dependencies, we recommend using a virtualenv. Once you have cloned the repository, change directories to the root of the repository and run
virtualenv -p python3 .venvOnce the virtual environment is created, activate it by running:
source .venv/bin/activateAny Python libraries installed will now be contained within this virtual environment. To deactivate the environment, simply run:
deactivateThen, install Emmental and any other python dependencies by running:
pip install -r requirements.txt