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README.md
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# Datacomp200m
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This is a smaller version of the [
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Filtering was done by taking all rows that had self similarity (inner product) above 0.32. This resulted in 213009083 (213 million) rows.
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The results of the datacomp paper suggest that filtering by CLIP score is better than random sampling.
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Included in this repo are search indices created using autofaiss, over the text and image embeddings.
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I would suggest using [embedding-reader](https://github.com/rom1504/embedding-reader) to load the text and image embeddings.
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# Datacomp200m
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This is a smaller version of the [datacomp_1b](https://huggingface.co/datasets/mlfoundations/datacomp_1b) dataset.
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Filtering was done by taking all rows that had self similarity (inner product) above 0.32. This resulted in 213009083 (213 million) rows.
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The results of the datacomp paper suggest that filtering by CLIP score is better than random sampling.
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Included in this repo are search indices created using autofaiss, over the text and image embeddings. There are two ways to access metadata, either in .parquet files in the `./metadata` directory, or the `./index/metadata.hdf5` hdf5 file.
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I would suggest using [embedding-reader](https://github.com/rom1504/embedding-reader) to load the text and image embeddings.
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