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Neural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit feedback by combining generalized matrix factorization and multi-layer perceptron networks through a shared final embedding layer. It captures both linear and non-linear interactions to model user preferences from historical data. The framework executes training and evaluation runs through a configuration-driven pipeline accessible via command-line interfaces, parsing hyperparameters such as learning rates, batch sizes, and latent dimensions. It optimizes implicit feedback mod
Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"
This repository provides an overview of all components from the paper OctoPack: Instruction Tuning Code Large Language Models. Link to 5-min video on the paper presented by Niklas Muennighoff.
Expanding natural instructions
Original Flan (2021) | The Flan Collection (2022) | Flan 2021 Citation | License
The main features of google-research/flan are: Evaluation and Benchmark Datasets, Instruction Tuning, RLHF Datasets.
Projects with overlapping indexed features include: hexiangnan/neural_collaborative_filtering — Neural collaborative filtering is a recommendation system framework that predicts user item preferences from implicit… anthropics/hh-rlhf — Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback". bigcode-project/octopack — This repository provides an overview of all components from the paper OctoPack: Instruction Tuning Code Large Language… bigscience-workshop/promptsource — Toolkit for creating, sharing and using natural language prompts. bigscience-workshop/xmtf — This repository provides an overview of all components used for the creation of BLOOMZ & mT0 and xP3 introduced in the… allenai/natural-instructions — Expanding natural instructions.