Datasetpublic

tantara/Nemotron-Personas-USA-Qwen3-0.6B-embedding

Nemotron-Personas-USA-Qwen3-0.6B-embedding Embeddings for nvidia/Nemotron-Personas-USA computed with Qwen/Qwen3-Embedding-0.6B. Details Source dataset: nvidia/Nemotron-Personas-USA Embedding model: Qwen/Qwen3-Embedding-0.6B Embedding dimension: 1024 Number of rows: 1000000 (first 1M rows of the 7M-row source dataset) Columns embedded (in dataset order): professional_persona, sports_persona, arts_persona, travel_persona, culinary_persona, persona… See the full description on the dataset page: https://huggingface.co/datasets/tantara/Nemotron-Personas-USA-Qwen3-0.6B-embedding.

sourceHugging Facecc-by-4.0updated 4mo agoView on Hugging Face

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Dataset Card

Nemotron-Personas-USA-Qwen3-0.6B-embedding

Embeddings for `nvidia/Nemotron-Personas-USA` computed with `Qwen/Qwen3-Embedding-0.6B`.

Details

—Source dataset: nvidia/Nemotron-Personas-USA
—Embedding model: Qwen/Qwen3-Embedding-0.6B
—Embedding dimension: 1024
—Number of rows: 1000000 (first 1M rows of the 7M-row source dataset)
—Columns embedded (in dataset order): professional_persona, sports_persona, arts_persona, travel_persona, culinary_persona, persona, cultural_background, skills_and_expertise, hobbies_and_interests, career_goals_and_ambitions, sex, age, marital_status, education_level, bachelors_field, occupation, city, state, zipcode, country

Schema

Column	Type	Description
`uuid`	string	Primary key — joins back to the source dataset
`embedding`	list<float32>	1024-dim embedding vector

Loading

python

from datasets import load_dataset
ds = load_dataset("tantara/Nemotron-Personas-USA-Qwen3-0.6B-embedding", split="train")
print(ds[0]["uuid"], ds[0]["embedding"][:5])