| --- |
| 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. |
|
|
