dejanseo/200k-wiki-data-embeddings-gemini

200k Wikidata Embeddings (Gemini)

Text embeddings for ~200,000 Wikidata entities, generated from each entity's English label with Google's gemini-embedding-001 model. Vectors are 768-dimensional and L2-normalized to unit length, so cosine similarity equals a plain dot product.

What's inside

Column	Type	Description
entity_id	string	Wikidata Q-identifier, e.g. Q42
label	string	English label, e.g. Douglas Adams
embedding	list<float32>[768]	Unit-length embedding of the label

• Rows: 199,998
• Dimensions: 768
• Normalization: L2 (every vector has norm 1.0)
• Source model: gemini-embedding-001 (native 3072-dim, requested at output_dimensionality=768, then L2-normalized)
• File: gemini_norm.parquet

The entities are a sample of the full Wikidata label set (~118.7M labelled entities). Only the label string was embedded — no descriptions, aliases, or statements were used.

Usage

from datasets import load_dataset

ds = load_dataset("dejanseo/200k-wiki-data-embeddings-gemini", split="train")
print(ds[0]["entity_id"], ds[0]["label"])
print(len(ds[0]["embedding"]))  # 768

Semantic search with cosine similarity (a dot product, since vectors are unit-length):

import numpy as np

emb = np.array(ds["embedding"], dtype=np.float32)   # (199998, 768)
ids = ds["entity_id"]
labels = ds["label"]

def nearest(query_vec, k=10):
    scores = emb @ np.asarray(query_vec, dtype=np.float32)  # cosine similarity
    top = np.argsort(-scores)[:k]
    return [(ids[i], labels[i], float(scores[i])) for i in top]

How it was built

1. English labels were pulled from a local Wikidata label database.
2. Each label was embedded via the Gemini Batch Embeddings API (gemini-embedding-001, output_dimensionality=768).
3. The returned vectors were L2-normalized to unit length and written to Parquet.

Because the vectors are truncated Matryoshka outputs (768 of the model's native 3072 dims) and then renormalized, they are directly usable for cosine / dot-product retrieval.

Companion dataset

A parallel set of embeddings for the same 199,998 entities was produced with the open google/embeddinggemma-300m model (also 768-dim, unit-length). Because the entity_id keys are identical, the two sets can be joined row-for-row for cross-model comparison.

Note on cross-model use: the two models place entities in differently-oriented spaces. Do not compare a Gemini vector directly against a Gemma vector — raw cross-model similarity is meaningless. Within a single model, similarity is well-behaved.

Licensing

Wikidata labels are released under CC0. The embedding vectors are derived outputs of Google's Gemini API and are subject to Google's applicable terms. Choose and set a license: value in the metadata above that reflects how you intend to distribute the derived vectors before publishing.

Citation

If you use this dataset, please cite the source model and Wikidata:

• Google, gemini-embedding-001
• Wikidata (Wikimedia Foundation)