erikkaum/nomic-unsupervised-embedded-10M

Nomic Unsupervised Embedded 10M

This dataset is a derived embedding dataset built from 10 million pairs from nomic-ai/nomic-embed-unsupervised-data. It contains text pairs (query, document) and their corresponding dense vector representations:

• query_embedding
• document_embedding

Dataset Summary

• Base dataset: nomic-ai/nomic-embed-unsupervised-data
• Rows: ~10M
• Embedding model: Qwen/Qwen3-Embedding-0.6B
• Vector dtype: float32
• Intended use: retrieval training/evaluation, similarity search, hard-negative mining, and embedding analysis

How It Was Created

Embeddings were generated by streaming rows from the source dataset and encoding:

• query with instruction-formatted query prompts (split-aware prompt selection)
• document as the candidate passage text

Schema

Expected columns include:

• original source fields (including query, document, split metadata, etc.)
• query_embedding: list<float32>
• document_embedding: list<float32>

Prompting Strategy for Query Embeddings

Prompt templates are selected by source split type:

• QA: "Given a web search query, retrieve relevant passages that answer the query"
• Duplicate: "Given a question, retrieve similar questions that ask the same thing"
• Semantic: "Given a text, retrieve passages about the same topic"

Limitations

• This is an automatically generated embedding dataset; no manual quality annotation was added here.
• Embeddings inherit biases and failure modes of the base model and source data.
• Some source texts may be truncated during encoding according to pipeline max-length settings.

Citation

If you use this dataset, please cite:

• the original source dataset: nomic-ai/nomic-embed-unsupervised-data
• the embedding model: Qwen/Qwen3-Embedding-0.6B
• this derived dataset repository