# squad

**Repository Path**: yukyin/squad

## Basic Information

- **Project Name**: squad
- **Description**: Starter code for Stanford CS224n default final project on SQuAD 2.0
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Setup

1. Make sure you have [Miniconda](https://docs.conda.io/en/latest/miniconda.html) installed
    1. Conda is a package manager that sandboxes your project’s dependencies in a virtual environment
    2. Miniconda contains Conda and its dependencies with no extra packages by default (as opposed to Anaconda, which installs some extra packages)

2. cd into src, run `conda env create -f environment.yml`
    1. This creates a Conda environment called `squad`

3. Run `source activate squad`
    1. This activates the `squad` environment
    2. Do this each time you want to write/test your code
  
4. Run `python setup.py`
    1. This downloads SQuAD 2.0 training and dev sets, as well as the GloVe 300-dimensional word vectors (840B)
    2. This also pre-processes the dataset for efficient data loading
    3. For a MacBook Pro on the Stanford network, `setup.py` takes around 30 minutes total  

5. Browse the code in `train.py`
    1. The `train.py` script is the entry point for training a model. It reads command-line arguments, loads the SQuAD dataset, and trains a model.
    2. You may find it helpful to browse the arguments provided by the starter code. Either look directly at the `parser.add_argument` lines in the source code, or run `python train.py -h`.