| # Datacomp200m | |
| This is a smaller version of the [datacomp_1b](https://huggingface.co/datasets/mlfoundations/datacomp_1b) dataset. | |
| Filtering was done by taking all rows that had self similarity (inner product) above 0.32. This resulted in 213009083 (213 million) rows. | |
| The results of the datacomp paper suggest that filtering by CLIP score is better than random sampling. | |
| 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. | |
| I would suggest using [embedding-reader](https://github.com/rom1504/embedding-reader) to load the text and image embeddings. |