https://github.com/wildltr/ptranking

Learning to Rank in PyTorch
https://github.com/wildltr/ptranking

learning-to-rank pytorch ranking

Last synced: 2 days ago
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Learning to Rank in PyTorch

• Host: GitHub
• URL: https://github.com/wildltr/ptranking
• Owner: wildltr
• License: mit
• Created: 2018-09-15T02:14:21.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-07-26T07:44:15.000Z (over 2 years ago)
• Last Synced: 2024-10-03T08:37:53.065Z (about 1 month ago)
• Topics: learning-to-rank, pytorch, ranking
• Language: Python
• Homepage: https://wildltr.github.io/ptranking/
• Size: 3.09 MB
• Stars: 471
• Watchers: 10
• Forks: 69
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# What's New?

- The recent representative methods (such as [MO4SRD](https://wildltr.github.io/ptranking/) and [DALETOR](https://dl.acm.org/doi/abs/10.1145/3442381.3449831)) for Search Result Diversification by directly optimizing the evaluation metric (e.g., alpha-nDCG) have been added. (02/22/2022)

- Different types of neural scoring functions are supported now, namely **pointwise neural scoring function** (mainly consists of feedforward layers) and **listwise neural scoring function** (mainly builds upon multi-head self-attention Layer). (02/22/2022)

# Introduction

This open-source project, referred to as **PTRanking** (Learning-to-Rank in PyTorch) aims to provide scalable and extendable implementations of typical learning-to-rank methods based on PyTorch. On one hand, this project enables a uniform comparison over several benchmark datasets leading to an in-depth understanding of previous learning-to-rank methods. On the other hand, this project makes it easy to develop and incorporate newly proposed models, so as to expand the territory of techniques on learning-to-rank.

**Key Features**:

- A number of representative learning-to-rank models for addressing **Ad-hoc Ranking** and **Search Result Diversification**, including not only the traditional optimization framework via empirical risk minimization but also the adversarial optimization framework

- Supports widely used benchmark datasets. Meanwhile, random masking of the ground-truth labels with a specified ratio is also supported

- Supports different metrics, such as Precision, MAP, nDCG, nERR, alpha-nDCG and ERR-IA.

- Highly configurable functionalities for fine-tuning hyper-parameters, e.g., grid-search over hyper-parameters of a specific model

- Provides easy-to-use APIs for developing a new learning-to-rank model

Please refer to the [documentation site](https://wildltr.github.io/ptranking/) for more details.