Learning to Rank in TensorFlow

Multi-Task Deep Neural Networks for Natural Language Understanding

A web app for ranking computer science departments according to their research output in selective venues.

A tensorflow implementation of Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.

allRank is a framework for training learning-to-rank neural models based on PyTorch.

Grade exams fast and accurately using a scanner 🖨 or your phone 🤳.

A collection of BM25 algorithm variants

Inference algorithms for models based on Luce's choice axiom

⭐Github Ranking⭐ Github stars and forks ranking list. Github Top100 stars list of different languages. Automatically updated daily. | Github仓库排名，每日自动更新

The 4th Place Solution to the 2019 ACM Recsys Challenge by Team RosettaAI

Ranking made easy

SEO: Python script + shell script and cronjob to check ranks on a daily basis

⭐️ Web frameworks for Python, most starred on Github

Ranking of Steam games which favors "hidden gems". Featured in PC Gamer.

Compute PageRank on ~3 billion Wikipedia links on off-the-shelf hardware.

Pairwise comparisons with flexible time-dynamics.

“Chorus” of recommendation models: a PyTorch framework for Top-K recommendation with implicit feedback.

DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks

Representation Learning and Pairwise Ranking for Implicit Feedback in Top-N Item Recommendation

Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search

💀 Root-me Hall Of Fame dashboard 💀

Python 3 implementation and documentation of the Hermina-Janos local graph clustering algorithm.

a python convertion from the ruby implementation of Rémi Coulom's Whole-History Rating (WHR) algorithm.

Performs pairwise preference ranking for a given trainfile and testfile with binary class labels (1 and not 1). The binary classification on the pairwise test data gives a prediction from each pair of test items: which of the two should be ranked higher. From these pairwise preferences a ranking can be created using a greedy sort algorithm.

🍡PE's Math Library

Obtain your app ranking inside App Store and Mac App Store in every country