Best Practices on Recommendation Systems
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Updated
Oct 9, 2024 - Python
Best Practices on Recommendation Systems
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Learning to Rank in TensorFlow
A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.
Multi-Task Deep Neural Networks for Natural Language Understanding
DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks
A Collection of BM25 Algorithms in Python
An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"
allRank is a framework for training learning-to-rank neural models based on PyTorch.
Fast, differentiable sorting and ranking in PyTorch
Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.
Fast Differentiable Sorting and Ranking
“Chorus” of recommendation models: a light and flexible PyTorch framework for Top-K recommendation.
Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems
multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.
BARS: Towards Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS
Deep Recommenders
PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models
A general purpose recommender metrics library for fair evaluation.
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