ΑθNLP 2019

Important: This repository is now archived!

Exercises for the lab sessions of ΑθNLP 2019. The labs will cover the following:

1. Part-of-Speech Tagging with the Perceptron algorithm
2. Part-of-Speech Tagging with the Structured Perceptron algorithm
3. Neural Encoding for Text Classification
4. Neural Language Modeling
5. Neural Machine Translation
6. Question Answering

Setup

You will need to have Python 3 installed on your machine; we recommend using Anaconda, which is available for the most common OS distributions.

For the first two labs we will be using vanilla Python (along with the standard scientific libarires, i.e., NumPy, SciPy, etc), while for the rest we will additionally be using PyTorch and AllenNLP.

Use the Anaconda command-line tools to create a new virtual environment with Python 3.6:

    conda create --name athnlp python=3.6

After the installation is complete, you should have a new virtual environment called athnlp in your Anaconda installation that you can activate using the following command: conda activate athnlp. Remember to execute this command before running the scripts in this repository.

Next, you should clone the repository to your computer:

    git clone https://github.com/athnlp/athnlp-labs

Finally, you should install all required dependencies. We provide a script that will help you setup your environment. Run the command: sh setup_dependencies.sh and it will automatically install the project dependencies for you. The script will download several data dependencies that might require some time to be installed.

Note: Installing AllenNLP on Mac OS can be tricky; check here for a possible solution.

Docker

If you prefer (or you are on Windows), you can install Docker and create a Docker image with the following commands:

• build it by running docker build -t athnlp - < Dockerfile
• get an interactive terminal on the image with docker run -i -t athnlp bash
• run commands as you normally would (remember this is a very minimal linux installation) If you want to run the image with a new version of the code, add the option --no-cache to the build. You need to do the wget commands from setup_dependencies.sh on your own. Make sure you give Docker enough disk space and memory