MultiQA is a project whose goal is to facilitate training and evaluating reading comprehension models over arbitrary sets of datasets. All datasets are in a single format, and it is accompanied by an AllenNLP DatasetReader and model that enable easy training and evaluation on multiple subsets of datasets.

This repository contains the code for our paper MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension.

This work was performed at The Allen Institute of Artificial Intelligence.

This project is constantly being improved. Contributions, comments and suggestions are welcome!

Link to the single format dataset are provided in the Train, Dev and Test columns. The BERT-Base column contains evaluation results (EM/F1) as well as a link to the trained model. The MultiQA-5Base column contain the link to the model (in the header) and evalution results for this model. This model is BERT-Base that has been trained on 5 datasets.

Datasets will be addeed weekly, so please stay tuned!

In order to simply train BERT on multiple datasets please use:

python multiqa.py train --datasets SQuAD1-1,NewsQA,SearchQA --cuda_device 0,1,2,3
python multiqa.py evaluate --model SQuAD1-1 --datasets SQuAD1-1,NewsQA,SearchQA --cuda_device 0

By default the output will be stored in models/datatset1_dataset2_... to change this please change --serialization_dir

Type python multiqa.py for additional options.

Note, this version uses the default multiqa format datasets stored in s3, to use your own dataset please see Readme for using allennlp core commands.

If you prefer using SQuAD2.0 format, or run the Pytorch-Trasformers models, please use:

python convert_multiqa_to_squad_format.py --datasets https://multiqa.s3.amazonaws.com/data/HotpotQA_dev.jsonl.gz --output_file data/squad_format/HotpotQA_dev.json

1. First, clone the repository:

   git clone https://github.com/alontalmor/MultiQA.git
   

2. Change your directory to where you cloned the files:

   cd MultiQA
   

3. Create a virtual environment with Python 3.6 or above:

   virtualenv venv --python=python3.7 (or python3.7 -m venv venv or conda create -n multiqa python=3.7)
   

4. Activate the virtual environment. You will need to activate the venv environment in each terminal in which you want to use WebAsKB.

   source venv/bin/activate (or source venv/bin/activate.csh or conda activate multiqa)
   

5. Install the required dependencies:

   pip install -r requirements.txt
   

The allennlp caching infra is used, so be sure to have enough disk space, and control the cache directory using ALLENNLP_CACHE_ROOT env variable.

This will take a dataset from it's original URL and output the same dataset in the MultiQA format.

python build_dataset.py --dataset_name HotpotQA --split train --output_file path/to/output.jsonl.gz --n_processes 10

first argument is the allennlp model, second is the preprocessed evalutaion file ( path/to/output.jsonl.gz in preprocess), then the dataset name (in order to create the official predictions format)

python predict.py --model https://multiqa.s3.amazonaws.com/models/BERTBase/SQuAD1-1.tar.gz --dataset https://multiqa.s3.amazonaws.com/data/SQuAD1-1_dev.jsonl.gz --dataset_name SQuAD

To predict only a the first N examples use --sample_size N

To add a GPU device simply append: --cuda_device 0

By default the output will be saved at results/DATASET_NAME/... You may also change the output filename and path using --prediction_filepath path/to/my/output

see Readme in the datasets folder.

see Readme in the models folder.

see Readme in the models folder.

Allennlp caching infra is used, so make sure to have enough disk space, and control the cache directory using ALLENNLP_CACHE_ROOT env variable.