You need Python 3.6.
Anaconda is strongly recommended for the following steps.
conda create -n qair python=3.6
source activate qair
conda install pytorch torchvision -c pytorch
If your machine support CUDA it will do the setup for you!
conda install gensim
conda install -c conda-forge spacy
python -m spacy download en
conda install -c conda-forge scikit-learn
```conda install tabulate``
Download the TRECQA and WikiQA dataset
Create a directory called original_data in the main directory and download the WikiQA dataset archive in it.
run the bash script:
sh get_data.sh
Download the embeddings of choice:
Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): glove.840B.300d.zip
for all the models but Severyn2016 (it requires the embeddings in https://github.com/aseveryn/deep-qa)
move the embeddings into a embs directory and convert them in textual format calling them glove.txt and alexi.bin respectfully.
process the dataset the dataset
sh process_data.sh
python -m qair.train configs/severyn2016.json wikiqa --name severyn_2016
Results are printed on screen and saved in the severyn_2016 directory
By now there are 3 model implemented (the original Severyn 2016 model) a CNN Baseline and a simplified version of the Relational CNN.