---
language:
- en
license: apache-2.0
size_categories:
- 1M<n<10M
task_categories:
- feature-extraction
tags:
- multimodal
- embeddings
- vector-search
- benchmark
- image-text
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: emb
    sequence: float32
  splits:
  - name: train
    num_examples: 1000000
  - name: test
    num_examples: 10000
configs:
- config_name: default
  data_files:
  - split: train
    path: train.parquet
  - split: test
    path: test.parquet
---

# Multimodal Embedding 1M Benchmark Dataset

A vector search benchmark dataset containing **1M base vectors** and **10K query vectors** with pre-computed ground truth, generated from multimodal (image + text) inputs.

## Dataset Description

Each embedding is produced by encoding an image-text pair into a single 4096-dimensional vector using [Qwen3-VL-Embedding-8B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B), a state-of-the-art multimodal embedding model.

- **Source data**: [pixparse/cc3m-wds](https://huggingface.co/datasets/pixparse/cc3m-wds) (Conceptual Captions 3M in WebDataset format)
- **Embedding model**: Qwen3-VL-Embedding-8B via vLLM (runner="pooling", dtype=bfloat16)
- **Input format**: Each sample consists of an image and its corresponding caption, embedded together as a multimodal input
- **Normalization**: L2 normalized

## Files

| File | Rows | Columns | Description |
|------|------|---------|-------------|
| `train.parquet` | 1,000,000 | `id` (int64), `emb` (list\<float32\>) | Base vectors from shards 0-199 |
| `test.parquet` | 10,000 | `id` (int64), `emb` (list\<float32\>) | Query vectors from shards 200-201 |
| `neighbors.parquet` | 10,000 | `id` (int64), `neighbors` (list\<int64\>) | Ground truth: top-100 nearest neighbors by Inner Product |

## Key Properties

| Property | Value |
|----------|-------|
| Dimension | 4096 |
| Distance metric | Inner Product (IP) |
| Base size | 1,000,000 |
| Query size | 10,000 |
| Top-K (ground truth) | 100 |
| Vector dtype | float32 |
| Base/Query overlap | None (disjoint shards) |

## Usage

```python
from datasets import load_dataset

# Load base and query splits
ds = load_dataset("WenxingZhu/multimodal-embedding-1M")
train = ds["train"]  # 1M base vectors
test = ds["test"]    # 10K query vectors

# Load ground truth
import pyarrow.parquet as pq
gt = pq.read_table("neighbors.parquet").to_pandas()

# Access embeddings
import numpy as np
base_emb = np.array(train["emb"])      # (1000000, 4096)
query_emb = np.array(test["emb"])      # (10000, 4096)
neighbors = np.array(gt["neighbors"].tolist())  # (10000, 100)
```

## Generation Details

- **Hardware**: 4x NVIDIA A100-80GB (DGX)
- **Inference**: vLLM v0.17.1, pooling mode, batch size 16
- **Throughput**: ~25 samples/sec per GPU
- **Total time**: ~2.75 hours for 1M embeddings
- **Prompt template**: System message "Represent the user's input." + User message with image and text content

## License

This dataset is released under the Apache 2.0 license. The source images and captions are from Conceptual Captions 3M.