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metmuseum/openaccess-embeddings-siglip2-oembeddings

metmuseum/openaccess-embeddings-siglip2-oembeddings Embeddings produced by google/siglip2-so400m-patch14-384 over metmuseum/openaccess, shaped to match the go-embeddingsdb Record struct (with the OEmbeddings spec inside attributes). Column Type Notes provider string "metmuseum" subject_id string metmuseum:{objectID} — the artwork depiction_id string metmuseum:{objectID}:primaryImage — the image model string HF model id embeddings list<float32> L2-normalised… See the full description on the dataset page: https://huggingface.co/datasets/metmuseum/openaccess-embeddings-siglip2-oembeddings.

sourceHugging Facecc0-1.0updated 5mo agoView on Hugging Face
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metmuseum/openaccess-embeddings-siglip2-oembeddings

Embeddings produced by google/siglip2-so400m-patch14-384 over `metmuseum/openaccess`, shaped to match the `go-embeddingsdb` Record struct (with the OEmbeddings spec inside attributes).

ColumnTypeNotes
providerstring"metmuseum"
subject_idstringmetmuseum:{objectID} — the artwork
depiction_idstringmetmuseum:{objectID}:primaryImage — the image
modelstringHF model id
embeddingslist&lt;float32&gt;L2-normalised, dim = 1152
createdint64Unix epoch seconds when this batch was generated
attributesmap&lt;string, string&gt;OEmbeddings spec fields (see below)

attributes struct

FieldRequiredSource
typeyesalways "image"
previewyes (URI)primaryImageSmall or primaryImage
subject_urlyesobjectURL (the metmuseum.org page)
subject_titleyesTitle
subject_creditlineyescreditLine
provider_nameyes"The Metropolitan Museum of Art"
provider_urlyeshttps://www.metmuseum.org/
depiction_urloptionalprimaryImage

Loading

python
from datasets import load_dataset

ds = load_dataset("metmuseum/openaccess-embeddings-siglip2-oembeddings", split="train")
row = ds[0]
print(row["subject_id"], row["model"], row["attributes"]["subject_title"])