Binary Passage Retriever (BPR) - an efficient passage retriever for open-domain question answering

Related tags

Deep Learningbpr
Overview

BPR

Binary Passage Retriever (BPR) is an efficient neural retrieval model for open-domain question answering. BPR integrates a learning-to-hash technique into Dense Passage Retriever (DPR) to represent the passage embeddings using compact binary codes rather than continuous vectors. It substantially reduces the memory size without a loss of accuracy tested on Natural Questions and TriviaQA datasets.

BPR was originally developed to improve the computational efficiency of the Sōseki question answering system submitted to the Systems under 6GB track in the NeurIPS 2020 EfficientQA competition. Please refer to our ACL 2021 paper for further technical details.

Installation

BPR can be installed using Poetry:

poetry install

The virtual environment automatically created by Poetry can be activated by poetry shell.

Alternatively, you can install required libraries using pip:

pip install -r requirements.txt

Trained Models

(coming soon)

Reproducing Experiments

Before you start, you need to download the datasets available on the DPR website into <DPR_DATASET_DIR>.

The experimental results on the Natural Questions dataset can be reproduced by running the commands provided in this section. We used a server with 8 NVIDIA Tesla V100 GPUs with 16GB memory in the experiments. The results on the TriviaQA dataset can be reproduced by changing the file names of the input dataset to the corresponding ones (e.g., nq-train.json -> trivia-train.json).

1. Building passage database

python build_passage_db.py \
    --passage_file=<DPR_DATASET_DIR>/wikipedia_split/psgs_w100.tsv \
    --output_file=<PASSAGE_DB_FILE>

2. Training BPR

python train_biencoder.py \
   --gpus=8 \
   --distributed_backend=ddp \
   --train_file=<DPR_DATASET_DIR>/retriever/nq-train.json \
   --eval_file=<DPR_DATASET_DIR>/retriever/nq-dev.json \
   --gradient_clip_val=2.0 \
   --max_epochs=40 \
   --binary

3. Building passage embeddings

python generate_embeddings.py \
   --biencoder_file=<BPR_CHECKPOINT_FILE> \
   --output_file=<EMBEDDING_FILE> \
   --passage_db_file=<PASSAGE_DB_FILE> \
   --batch_size=4096 \
   --parallel

4. Evaluating BPR

python evaluate_retriever.py \
    --binary_k=1000 \
    --biencoder_file=<BPR_CHECKPOINT_FILE> \
    --embedding_file=<EMBEDDING_FILE> \
    --passage_db_file=<PASSAGE_DB_FILE> \
    --qa_file=<DPR_DATASET_DIR>/retriever/qas/nq-test.csv \
    --parallel

5. Creating dataset for reader

python evaluate_retriever.py \
    --binary_k=1000 \
    --biencoder_file=<BPR_CHECKPOINT_FILE> \
    --embedding_file=<EMBEDDING_FILE> \
    --passage_db_file=<PASSAGE_DB_FILE> \
    --qa_file=<DPR_DATASET_DIR>/retriever/qas/nq-train.csv \
    --output_file=<READER_TRAIN_FILE> \
    --top_k=200 \
    --parallel

python evaluate_retriever.py \
    --binary_k=1000 \
    --biencoder_file=<BPR_CHECKPOINT_FILE> \
    --embedding_file=<EMBEDDING_FILE> \
    --passage_db_file=<PASSAGE_DB_FILE> \
    --qa_file=<DPR_DATASET_DIR>/retriever/qas/nq-dev.csv \
    --output_file=<READER_DEV_FILE> \
    --top_k=200 \
    --parallel

python evaluate_retriever.py \
    --binary_k=1000 \
    --biencoder_file=<BPR_CHECKPOINT_FILE> \
    --embedding_file=<EMBEDDING_FILE> \
    --passage_db_file=<PASSAGE_DB_FILE> \
    --qa_file==<DPR_DATASET_DIR>/retriever/qas/nq-test.csv \
    --output_file=<READER_TEST_FILE> \
    --top_k=200 \
    --parallel

6. Training reader

python train_reader.py \
   --gpus=8 \
   --distributed_backend=ddp \
   --train_file=<READER_TRAIN_FILE> \
   --validation_file=<READER_DEV_FILE> \
   --test_file=<READER_TEST_FILE> \
   --learning_rate=2e-5 \
   --max_epochs=20 \
   --accumulate_grad_batches=4 \
   --nq_gold_train_file=<DPR_DATASET_DIR>/gold_passages_info/nq_train.json \
   --nq_gold_validation_file=<DPR_DATASET_DIR>/gold_passages_info/nq_dev.json \
   --nq_gold_test_file=<DPR_DATASET_DIR>/gold_passages_info/nq_test.json \
   --train_batch_size=1 \
   --eval_batch_size=2 \
   --gradient_clip_val=2.0

7. Evaluating reader

python evaluate_reader.py \
    --gpus=8 \
    --distributed_backend=ddp \
    --checkpoint_file=<READER_CHECKPOINT_FILE> \
    --eval_batch_size=1

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Citation

If you find this work useful, please cite the following paper:

@inproceedings{yamada2021bpr,
  title={Efficient Passage Retrieval with Hashing for Open-domain Question Answering},
  author={Ikuya Yamada and Akari Asai and Hannaneh Hajishirzi},
  booktitle={ACL},
  year={2021}
}
Owner
Studio Ousia
Studio Ousia
GitHub Repository
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification [pdf] The official repository for Self-Supervised Pre-Training for Transfo

Hao Luo 116 Jan 04, 2023
MiraiML: asynchronous, autonomous and continuous Machine Learning in Python

MiraiML Mirai: future in japanese. MiraiML is an asynchronous engine for continuous & autonomous machine learning, built for real-time usage. Usage In

Arthur Paulino 25 Jul 27, 2022
TensorFlow (Python) implementation of DeepTCN model for multivariate time series forecasting.

DeepTCN TensorFlow TensorFlow (Python) implementation of multivariate time series forecasting model introduced in Chen, Y., Kang, Y., Chen, Y., & Wang

Flavia Giammarino 21 Dec 19, 2022
PyTorch Implementation of PIXOR: Real-time 3D Object Detection from Point Clouds

PIXOR: Real-time 3D Object Detection from Point Clouds This is a custom implementation of the paper from Uber ATG using PyTorch 1.0. It represents the

Philip Huang 270 Dec 14, 2022
DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Download

Bubbliiiing 31 Nov 25, 2022
Offline Reinforcement Learning with Implicit Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning This repository contains the official implementation of Offline Reinforcement Learning with Im

Ilya Kostrikov 125 Dec 31, 2022
Keras Image Embeddings using Contrastive Loss

Image to Embedding projection in vector space. Implementation in keras and tensorflow of batch all triplet loss for one-shot/few-shot learning.

Shravan Anand K 5 Mar 21, 2022
Evaluation suite for large-scale language models.

This repo contains code for running the evaluations and reproducing the results from the Jurassic-1 Technical Paper (see blog post), with current support for running the tasks through both the AI21 S

71 Dec 17, 2022
Using fully convolutional networks for semantic segmentation with caffe for the cityscapes dataset

Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset How to get started Download the

Simon Guist 27 Jun 06, 2022
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
A benchmark for the task of translation suggestion

WeTS: A Benchmark for Translation Suggestion Translation Suggestion (TS), which provides alternatives for specific words or phrases given the entire d

zhyang 55 Dec 24, 2022
Aligning Latent and Image Spaces to Connect the Unconnectable

About This repo contains the official implementation of the Aligning Latent and Image Spaces to Connect the Unconnectable paper. It is a GAN model whi

Ivan Skorokhodov 203 Jan 03, 2023
Bot developed in Python that automates races in pegaxy.

español | português About it: This is a fork from pega-racing-bot. This bot, developed in Python, is to automate races in pegaxy. The game developers

4 Apr 08, 2022
Codes for 'Dual Parameterization of Sparse Variational Gaussian Processes'

Dual Parameterization of Sparse Variational Gaussian Processes Documentation | Notebooks | API reference Introduction This repository is the official

AaltoML 7 Dec 23, 2022
Chinese license plate recognition

AgentCLPR 简介 一个基于 ONNXRuntime、AgentOCR 和 License-Plate-Detector 项目开发的中国车牌检测识别系统。 车牌识别效果 支持多种车牌的检测和识别（其中单层车牌识别效果较好）： 单层车牌： [[[[373, 282], [69, 284],

AgentMaker 26 Dec 25, 2022
The implementation of the lifelong infinite mixture model

Lifelong infinite mixture model 📋 This is the implementation of the Lifelong infinite mixture model 📋 Accepted by ICCV 2021 Title : Lifelong Infinit

Fei Ye 5 Oct 20, 2022
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

The Hypersim Dataset For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real i

Apple 1.3k Jan 04, 2023
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Jan 03, 2023
This package implements THOR: Transformer with Stochastic Experts.

THOR: Transformer with Stochastic Experts This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts. Installation

Microsoft 45 Nov 22, 2022
Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

Davis Rempe 367 Dec 24, 2022