alexpod1000 / SQuAD-QA

Question answering on SQuAD 1.1 dataset by finetuning BERT models. Training is supported both on GPU and on Colab TPU.

SQuAD-QA

Question answering on SQuAD 1.1 dataset

Downloading Dataset

In order to download SQuAD 1.1 dataset, run the following commands:

wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json

This will download both training dataset (40 MB) and a development dataset (4 MB).

Setting the environment

To setup the environment run the following commands:

conda create -n squad_qa_pytorch python=3.8
conda activate squad_qa_pytorch

conda install pytorch cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt

# setup jupyter notebook support
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=squad_qa_pytorch

Training and evaluation

Training of BERT models is supported via Train_model_GPU_BERT.ipynb and Train_model_TPU_BERT.ipynb notebooks.

The supported experiments can be found inside this configurations file and include the following models: BERT, DistilBERT, ALBERT, DistilROBERTA.

Results

The trained models obtain the following results on SQuAD-1.1 dev benchmark:

Model	F1	EM
ALBERT	86.97	78.39
BERT	81.51	71.82
DistilBERT	78.96	68.68
DistilROBERTA	87.74	80.39
DistilROBERTA + Linear Layer	88.09	80.93