Skip to content

Latest commit

 

History

History
61 lines (48 loc) · 1.48 KB

README.md

File metadata and controls

61 lines (48 loc) · 1.48 KB
Raw

A Repo for Benchmarking News Recommendation on MIND

This repository is ready-to-use for designing->training->testing a News Recommendation Model on MIND dataset. It offers comprehensive classes and utilities to load data and train/evaluate the model.

Environment

python=3.8.11
torch==1.9.1

Instruction

# manager will email you the training result, so you should add your gmail and password as follows:
mkdir data/configs
echo "email = '[email protected]'" >> data/configs/email.py
echo "password = 'your password'" >> data/configs/email.py

# train a basic twotower model with CNN news encoder and LSTM user encoder
python twotower.py

# test
python twotower.py -m test
  • specify -p=/your/path/to/MIND/root
  • specify -d=x if you want to train/test on the x-th gpu
  • specify -ws=x if you want to train/test with x gpus
  • for more hyper parameters, run python twotower.py --help

How to define your own model

  1. design your model in models/ referring to TwoTower.py
  2. write a script in ./ referring to twotower.py

Basic Introduction

Preprocess

in utils/MIND.py

  • tokenize
  • generate cache

Manager

in utils/Manager.py

  • handle hyper parameters
  • prepare dataloaders
  • handle the training/evaluation process

News Encoder

in models/Enocders

  • CNN
  • RNN
  • Multi Head Attention
  • Transformer
  • Pooling

User Encoder

in models/Enocders

  • LSTM
  • MHA
  • LSTUR
  • Pooling