| --- |
| 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 |
|
|
