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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