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Official repo to On the Generalization Ability of Retrieval-Enhanced Transformers

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Data Science Project Repository Template

This repo constitutes a skeleton for a typical ML/DS project. Docker is a first class citizen and can be customized by editing the provided Dockerfile.

When starting a new project, please do the following:

  1. On GitHub, create your own repository from this template by clicking the "Use this template" button
  2. Update DOCKER_IMAGE_NAME in start.sh
  3. Build and start a docker container:
./start.sh [--gpu] [--notebook] [--tensorboard] [-v|--mount /host/path:/container/path] [--detach]
  1. Start a development container in VS Code: There are two ways this can be done.
    • Attach to the already running container (preferred when container is running on remote host)
      • In VS Code, install the Remote-Containers extention
      • Run Remote-Containers: Attach to Running Container... (F1). Select the newly created container
      • In the Explorer pane, click Open Folder and type the workspace directory (by default mounted to /workspace)
    • Let VS Code manage the container (preferred for local development)
      • In VS Code, install the Remote-Containers extention
      • Update name in .devcontainer/devcontainer.json to the value of DOCKER_IMAGE_NAME
      • Run Remote-Containers: Reopen in container (F1). Select the newly created container

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  • Python 91.6%
  • Makefile 5.4%
  • Shell 2.1%
  • Dockerfile 0.9%