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25 lines
1.2 KiB
Markdown
25 lines
1.2 KiB
Markdown
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Twhin in torchrec
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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.
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We obtain entity embeddings based on a variety of graph data within Twitter such as:
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"User follows User"
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"User favorites Tweet"
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"User clicks Advertisement"
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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:
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https://huggingface.co/datasets/Twitter/TwitterFollowGraph
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https://huggingface.co/datasets/Twitter/TwitterFaveGraph
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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.
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The location of the data must be specified in the configuration yaml files in projects/twhin/configs.
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Workflow
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========
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- Build local development images `./scripts/build_images.sh`
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- Run with `./scripts/docker_run.sh`
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- Iterate in image with `./scripts/idocker.sh`
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- Run tests with `./scripts/docker_test.sh`
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