Datasets:

VDBBench
/

multimodal-embedding-10M

task_categories:
  - feature-extraction
language:
  - en
license: apache-2.0
size_categories:
  - 10M<n<100M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet
      - split: neighbors
        path: data/neighbors.parquet
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: emb
      sequence: float32
  splits:
    - name: train
      num_examples: 10000000
    - name: test
      num_examples: 10000
    - name: neighbors
      num_examples: 10000
tags:
  - multimodal
  - embeddings
  - vector-search
  - benchmark
  - image-text

Multimodal Embedding 10M Benchmark Dataset

A large-scale vector search benchmark dataset containing 10M base vectors and 10K query vectors with pre-computed ground truth (top-100 nearest neighbors by Inner Product), generated from multimodal (image + text) inputs.

Dataset Summary

Property	Value
Base Vectors	10,000,000
Query Vectors	10,000
Dimension	4,096
Distance Metric	Inner Product (IP)
Top-K Ground Truth	100
Vector dtype	float32
Embedding Model	Qwen3-VL-Embedding-8B
Source Data	pixparse/cc12m-wds (Conceptual Captions 12M)
Base/Query Overlap	Yes (0.1%, query shards 40-41 included in base)

Splits

Split	Rows	Description
`train`	10,000,000	Base vectors (20 parquet shards, 500K rows each)
`test`	10,000	Query vectors
`neighbors`	10,000	Ground truth: top-100 nearest neighbor IDs per query

Schema

train / test:

Column	Type	Description
`id`	int64	Sequential identifier (0-indexed)
`emb`	list<float32>	4096-dim L2-normalized embedding

neighbors:

Column	Type	Description
`id`	int64	Query ID (matches test split)
`neighbors`	list<int64>	Top-100 base vector IDs by IP score (descending)

Usage

from datasets import load_dataset
import numpy as np

ds = load_dataset("WenxingZhu/multimodal-embedding-10M")

# Base and query embeddings
base_emb = np.array(ds["train"]["emb"])    # (10_000_000, 4096) - streams from 20 shards
query_emb = np.array(ds["test"]["emb"])    # (10_000, 4096)

# Ground truth neighbors
neighbors = ds["neighbors"]["neighbors"]   # list of 10K lists, each 100 int64 IDs

Streaming (recommended for 10M)

from datasets import load_dataset

ds = load_dataset("WenxingZhu/multimodal-embedding-10M", split="train", streaming=True)
for row in ds:
    emb = row["emb"]  # list of 4096 floats
    break

Generation Details

Model: Qwen3-VL-Embedding-8B via vLLM (pooling runner, bfloat16)
Input: Image-text pairs from cc12m-wds WebDataset shards, streamed from HuggingFace
Normalization: L2 normalized post-embedding
Hardware: 6x NVIDIA A100-80GB (DGX)
Throughput: ~25 samples/sec per GPU
Total Generation Time: ~3 days for 10M embeddings
Ground Truth: Brute-force inner product, chunked numpy computation

multimodal-embedding-1M — 1M version from cc3m-wds

License

Apache 2.0