Datasets:
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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](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) |
| Source Data | [pixparse/cc12m-wds](https://huggingface.co/datasets/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
```python
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)
```python
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
## Related
- [multimodal-embedding-1M](https://huggingface.co/datasets/WenxingZhu/multimodal-embedding-1M) — 1M version from cc3m-wds
## License
Apache 2.0
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