Update dataset card

README.md

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+---
+license: cc0-1.0
+pretty_name: The Met Open Access — apple-mobileclip embeddings
+tags:
+  - art
+  - museum
+  - embeddings
+  - apple-mobileclip
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: default/train/apple-mobileclip-*.parquet
+---
+
+# metmuseum/openaccess-embeddings-apple-mobileclip
+
+Image embeddings for every public-domain artwork in [`metmuseum/openaccess`](https://huggingface.co/datasets/metmuseum/openaccess), produced by **apple/MobileCLIP-S2-OpenCLIP**.
+
+| Column | Type | Notes |
+|--------|------|-------|
+| `objectID` | int64 | Primary key — matches `objectID` in `metmuseum/openaccess` |
+| `embedding` | list<float32> | L2-normalised, dim = 512 |
+| `model` | string | Source model id |
+| `dim` | int32 | Embedding dimension |
+
+Image bytes are **not** stored here; join against the main dataset to recover them.
+
+Embedding spec: dim=512, expected image size=256px.
+
+## Joining with the main dataset
+
+```python
+from datasets import load_dataset
+
+meta = load_dataset("metmuseum/openaccess", split="train")
+emb  = load_dataset("metmuseum/openaccess-embeddings-apple-mobileclip", split="train")
+
+# Build an objectID -> embedding lookup, then attach to the metadata rows.
+lookup = {r["objectID"]: r["embedding"] for r in emb}
+joined = meta.map(lambda r: {"embedding": lookup.get(r["objectID"])})
+print(joined[0].keys())
+```
+
+## Nearest-neighbour example
+
+```python
+import numpy as np
+from datasets import load_dataset
+
+emb = load_dataset("metmuseum/openaccess-embeddings-apple-mobileclip", split="train")
+ids = np.array(emb["objectID"])
+mat = np.array(emb["embedding"], dtype=np.float32)  # already L2-normalised
+
+query = mat[0]
+scores = mat @ query
+top = np.argsort(-scores)[:10]
+print(list(zip(ids[top].tolist(), scores[top].tolist())))
+```
+
+Generated by [`et-openaccess-embeddings`](https://github.com/metmuseum/et-openaccess-embeddings).