Answering multiple choice questions with Language Models.
- 🚧 This project is currently under development. Stay tuned ! 🤩
- Refactored data subpackage, the library now supports RACE, Synonym, Swag and ARC data sets.
- Upgrade to
transformers==2.10.0.
pip install mcqagit clone https://github.com/mcqa-suite/mcqa.git
cd mcQA
pip install -e .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) :
| Context sentence | Choice 1 | Choice 2 | Choice 3 | Label |
|---|---|---|---|---|
| People do what during their time off from work? | take trips | brow shorter | become hysterical | take trips |
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)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)preds = mdl.predict(test_dataset, eval_batch_size=32)from sklearn.metrics import accuracy_score
from mcqa.data import get_labels
print(accuracy_score(preds, get_labels(train_dataset)))Read our Contributing Guidelines.
@misc{Taycir2019,
author = {mcQA-suite},
title = {mcQA},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/mcQA-suite/mcQA/}}
}