filename stringclasses 4 values | asset_type stringclasses 1 value | dataset_name float64 | go_date stringdate 2020-06-01 00:00:00 2025-06-01 00:00:00 | edition stringclasses 1 value | method stringclasses 2 values | bytes int64 40.7M 150M | sha256 stringclasses 4 values | note float64 |
|---|---|---|---|---|---|---|---|---|
go-basic-2020-06-01.sbert.npy | go_embeddings | null | 2020-06-01 | basic | sbert | 147,729,827 | 695324decb28e1cc3d09f73f2d9b12a5ef5d3e4b22f4ee874c7b3fe8b266250c | null |
go-basic-2020-06-01.stargo.npy | go_embeddings | null | 2020-06-01 | basic | stargo | 47,515,247 | ba3c45a1f7c9c67313b2fa353582958954a2942f84df65122a26fa6b4d0ce549 | null |
go-basic-2025-06-01.sbert.npy | go_embeddings | null | 2025-06-01 | basic | sbert | 150,047,561 | ba258a87b601c08cfa8f851004a514ef18bd9dd18c0a11c01ac0a3b2ea7a5c3c | null |
go-basic-2025-06-01.stargo.npy | go_embeddings | null | 2025-06-01 | basic | stargo | 40,699,399 | 86367c3c4cb44e53ad86302a95f7508727808fddae885f0e23d392ebc3a2583b | null |
stargo-embeddings
Dataset repository containing STAR-GO related embedding assets. The metadata.csv is loadable via datasets.load_dataset, while large binaries (e.g. .h5, .npy) are stored as downloadable files.
How to use
Load the metadata table:
from datasets import load_dataset
ds = load_dataset("<your-org-or-username>/<your-dataset-repo>")
print(ds)
Download the large binary assets referenced in the table with hf_hub_download.
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