cdQA: Closed Domain Question Answering

An End-To-End Closed Domain Question Answering System. Built on top of the HuggingFace transformers library.

⛔ [NOT MAINTAINED] This repository is no longer maintained, but is being kept around for educational purposes. If you want a maintained alternative to cdQA check out: https://github.com/deepset-ai/haystack

cdQA in details

If you are interested in understanding how the system works and its implementation, we wrote an article on Medium with a high-level explanation.

We also made a presentation during the #9 NLP Breakfast organised by Feedly. You can check it out here.

Table of Contents

• Installation
  • With pip
  • From source
  • Hardware Requirements
• Getting started
  • Preparing your data
    • Manual
    • With converters
  • Downloading pre-trained models
  • Training models
  • Making predictions
  • Evaluating models
• Notebook Examples
• Deployment
  • Manual
• Contributing
• References
• LICENSE

Installation

With pip

pip install cdqa

From source

git clone https://github.com/cdqa-suite/cdQA.git
cd cdQA
pip install -e .

Hardware Requirements

Experiments have been done with:

• CPU 👉 AWS EC2 t2.medium Deep Learning AMI (Ubuntu) Version 22.0
• GPU 👉 AWS EC2 p3.2xlarge Deep Learning AMI (Ubuntu) Version 22.0 + a single Tesla V100 16GB.

Getting started

Preparing your data

Manual

To use cdQA you need to create a pandas dataframe with the following columns:

title	paragraphs
The Article Title	[Paragraph 1 of Article, ... , Paragraph N of Article]

With converters

The objective of cdqa converters is to make it easy to create this dataframe from your raw documents database. For instance the pdf_converter can create a cdqa dataframe from a directory containing .pdf files:

from cdqa.utils.converters import pdf_converter

df = pdf_converter(directory_path='path_to_pdf_folder')

You will need to install Java OpenJDK to use this converter. We currently have converters for:

• pdf
• markdown

We plan to improve and add more converters in the future. Stay tuned!

Downloading pre-trained models and data

You can download the models and data manually from the GitHub releases or use our download functions:

from cdqa.utils.download import download_squad, download_model, download_bnpp_data

directory = 'path-to-directory'

# Downloading data
download_squad(dir=directory)
download_bnpp_data(dir=directory)

# Downloading pre-trained BERT fine-tuned on SQuAD 1.1
download_model('bert-squad_1.1', dir=directory)

# Downloading pre-trained DistilBERT fine-tuned on SQuAD 1.1
download_model('distilbert-squad_1.1', dir=directory)

Training models

Fit the pipeline on your corpus using the pre-trained reader:

import pandas as pd
from ast import literal_eval
from cdqa.pipeline import QAPipeline

df = pd.read_csv('your-custom-corpus-here.csv', converters={'paragraphs': literal_eval})

cdqa_pipeline = QAPipeline(reader='bert_qa.joblib') # use 'distilbert_qa.joblib' for DistilBERT instead of BERT
cdqa_pipeline.fit_retriever(df=df)

If you want to fine-tune the reader on your custom SQuAD-like annotated dataset:

cdqa_pipeline = QAPipeline(reader='bert_qa.joblib') # use 'distilbert_qa.joblib' for DistilBERT instead of BERT
cdqa_pipeline.fit_reader('path-to-custom-squad-like-dataset.json')

Save the reader model after fine-tuning:

cdqa_pipeline.dump_reader('path-to-save-bert-reader.joblib')

Making predictions

To get the best prediction given an input query:

cdqa_pipeline.predict(query='your question')

To get the N best predictions:

cdqa_pipeline.predict(query='your question', n_predictions=N)

There is also the possibility to change the weight of the retriever score versus the reader score in the computation of final ranking score (the default is 0.35, which is shown to be the best weight on the development set of SQuAD 1.1-open)

cdqa_pipeline.predict(query='your question', retriever_score_weight=0.35)

Evaluating models

In order to evaluate models on your custom dataset you will need to annotate it. The annotation process can be done in 3 steps:

1. Convert your pandas DataFrame into a json file with SQuAD format:

   from cdqa.utils.converters import df2squad
   
   json_data = df2squad(df=df, squad_version='v1.1', output_dir='.', filename='dataset-name')

2. Use an annotator to add ground truth question-answer pairs:

   Please refer to our cdQA-annotator, a web-based annotator for closed-domain question answering datasets with SQuAD format.

3. Evaluate the pipeline object:

   from cdqa.utils.evaluation import evaluate_pipeline
   
   evaluate_pipeline(cdqa_pipeline, 'path-to-annotated-dataset.json')

4. Evaluate the reader:

   from cdqa.utils.evaluation import evaluate_reader
   
   evaluate_reader(cdqa_pipeline, 'path-to-annotated-dataset.json')

Notebook Examples

We prepared some notebook examples under the examples directory.

You can also play directly with these notebook examples using Binder or Google Colaboratory:

Notebook	Hardware	Platform
[1] First steps with cdQA	CPU or GPU
[2] Using the PDF converter	CPU or GPU
[3] Training the reader on SQuAD	GPU

Binder and Google Colaboratory provide temporary environments and may be slow to start but we recommend them if you want to get started with cdQA easily.

Deployment

Manual

You can deploy a cdQA REST API by executing:

export dataset_path=path-to-dataset.csv
export reader_path=path-to-reader-model

FLASK_APP=api.py flask run -h 0.0.0.0

You can now make requests to test your API (here using HTTPie):

http localhost:5000/api query=='your question here'

If you wish to serve a user interface on top of your cdQA system, follow the instructions of cdQA-ui, a web interface developed for cdQA.

Contributing

Read our Contributing Guidelines.

References

Type	Title	Author	Year
📹 Video	Stanford CS224N: NLP with Deep Learning Lecture 10 – Question Answering	Christopher Manning	2019
📰 Paper	Reading Wikipedia to Answer Open-Domain Questions	Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes	2017
📰 Paper	Neural Reading Comprehension and Beyond	Danqi Chen	2018
📰 Paper	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova	2018
📰 Paper	Contextual Word Representations: A Contextual Introduction	Noah A. Smith	2019
📰 Paper	End-to-End Open-Domain Question Answering with BERTserini	Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, Jimmy Lin	2019
📰 Paper	Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering	Wei Yang, Yuqing Xie, Luchen Tan, Kun Xiong, Ming Li, Jimmy Lin	2019
📰 Paper	Passage Re-ranking with BERT	Rodrigo Nogueira, Kyunghyun Cho	2019
📰 Paper	MRQA: Machine Reading for Question Answering	Jonathan Berant, Percy Liang, Luke Zettlemoyer	2019
📰 Paper	Unsupervised Question Answering by Cloze Translation	Patrick Lewis, Ludovic Denoyer, Sebastian Riedel	2019
💻 Framework	Scikit-learn: Machine Learning in Python	Pedregosa et al.	2011
💻 Framework	PyTorch	Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan	2016
💻 Framework	Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.	Hugging Face	2018

LICENSE

Apache-2.0