mcQA : Multiple Choice Questions Answering

Answering multiple choice questions with Language Models.

News 📢

• 🚧 This project is currently under development. Stay tuned ! 🤩

Jun 6th, 2020

• Refactored data subpackage, the library now supports RACE, Synonym, Swag and ARC data sets.
• Upgrade to transformers==2.10.0.

Installation

With pip

pip install mcqa

From source

git clone https://github.com/mcqa-suite/mcqa.git
cd mcQA
pip install -e .

Getting started

Data preparation

To train a mcQA model, you need to create a csv file with n+2 columns, n being the number of choices for each question. The first column should be the context sentence, the n following columns should be the choices for that question and the last column is the selected answer.

Below is an example of a 3 choice question (taken from the CoS-E dataset) :

If you have a trained mcQA model and want to infer on a dataset, it should have the same format as the train data, but the label column.

See example data preparation below:

from mcqa.data import MCQAData

mcqa_data = MCQAData(bert_model="bert-base-uncased", lower_case=True, max_seq_length=256) 
                     
train_dataset = mcqa_data.read(data_file='swagaf/data/train.csv', is_training=True)
test_dataset = mcqa_data.read(data_file='swagaf/data/test.csv', is_training=False)

Model training

from mcqa.models import Model

mdl = Model(bert_model="bert-base-uncased", device="cuda") 
            
mdl.fit(train_dataset, train_batch_size=32, num_train_epochs=20)

Prediction

preds = mdl.predict(test_dataset, eval_batch_size=32)

Evaluation

from sklearn.metrics import accuracy_score
from mcqa.data import get_labels

print(accuracy_score(preds, get_labels(train_dataset)))

References

LICENSE

Apache-2.0

Contributing

Read our Contributing Guidelines.

Citation

@misc{Taycir2019,
  author = {mcQA-suite},
  title = {mcQA},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/mcQA-suite/mcQA/}}
}