Stars
A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
Lime: Explaining the predictions of any machine learning classifier
Repository hosting code for "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).
Build resilient language agents as graphs.
ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)
Simulator for training and evaluation of Recommender Systems
A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io
Flexible Python configuration system. The last one you will ever need.
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
Graph Neural Network Library for PyTorch
Python based GBDT implementation on GPU. Efficient multioutput (multiclass/multilabel/multitask) training
This is a repository of public data sources for Recommender Systems (RS).
Acceptance rates for the major AI conferences
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.
HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
A Python scikit for building and analyzing recommender systems
A Python implementation of LightFM, a hybrid recommendation algorithm.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
rpclib is a modern C++ msgpack-RPC server and client library
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
oneAPI Level Zero Specification Headers and Loader
Parse numbers written in natural language
An open source library for deep learning end-to-end dialog systems and chatbots.
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing …
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf