Twhin in torchrec

This project contains code for pretraining dense vector embedding features for Twitter entities. Within Twitter, these embeddings are used for candidate retrieval and as model features in a variety of recommender system models.

We obtain entity embeddings based on a variety of graph data within Twitter such as: "User follows User" "User favorites Tweet" "User clicks Advertisement"

While we cannot release the graph data used to train TwHIN embeddings due to privacy restrictions, heavily subsampled, anonymized open-sourced graph data can used: https://huggingface.co/datasets/Twitter/TwitterFollowGraph https://huggingface.co/datasets/Twitter/TwitterFaveGraph

The code expects parquet files with three columns: lhs, rel, rhs that refer to the vocab index of the left-hand-side node, relation type, and right-hand-side node of each edge in a graph respectively.

The location of the data must be specified in the configuration yaml files in projects/twhin/configs.

Workflow

Build local development images ./scripts/build_images.sh
Run with ./scripts/docker_run.sh
Iterate in image with ./scripts/idocker.sh
Run tests with ./scripts/docker_test.sh

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