| ---
|
| license: apache-2.0
|
| dataset_info:
|
| features:
|
| - name: parent_asin
|
| dtype: string
|
| - name: value
|
| list: float64
|
| - name: main_category
|
| dtype: string
|
| - name: title
|
| dtype: string
|
| - name: average_rating
|
| dtype: float64
|
| - name: rating_number
|
| dtype: float64
|
| - name: description
|
| dtype: string
|
| - name: price
|
| dtype: float64
|
| - name: categories
|
| dtype: string
|
| - name: image_url
|
| dtype: string
|
| splits:
|
| - name: train
|
| num_bytes: 3482499106
|
| num_examples: 100000
|
| download_size: 2309398330
|
| dataset_size: 3482499106
|
| configs:
|
| - config_name: 10k
|
| data_files:
|
| - split: train
|
| path: "benchmark-10k/*.parquet"
|
| - config_name: 100k
|
| data_files:
|
| - split: train
|
| path: "benchmark-100k/*.parquet"
|
| - config_name: 1M
|
| data_files:
|
| - split: train
|
| path: "benchmark-1M/*.parquet"
|
| - config_name: 10M
|
| data_files:
|
| - split: train
|
| path: "benchmark-10M/*.parquet"
|
| ---
|
|
|
| # Vector Search Benchmarks
|
|
|
| This repo contains datasets for benchmarking vector search performance, to help Superlinked prioritize integration partners.
|
| For performing actual benchmarking on this dataset, see the [github repository README](https://github.com/superlinked/external-benchmarks).
|
|
|
| ## Overview
|
|
|
| We reviewed a number of publicly available datasets and noted 3 core problems + here is how this dataset fixes them:
|
|
|
| |Problems of other vector search benchmarks| How this dataset solves it |
|
| |-|--------------------------------------------------------------------|
|
| |Not enough metadata of various types makes it hard to test filter performance| 3 number, 1 categorical, 3 text, 1 image column |
|
| |Vectors too small, while SOTA models usually output 2k+ even 4k+ dims| 4154 dims |
|
| |Dataset too small, especially if larger vectors are used| 100k, 1M and 10M item variants, all sampled from the large dataset |
|
|
|
| ## Available Datasets
|
|
|
| ### Product data
|
|
|
| The `data_dir`s contain parquet files with the metadata and vectors.
|
|
|
| | Dataset | Records | # Files | Size |
|
| |----------------|------------|---------|---------|
|
| | benchmark_10k | 10,000 | 100 | ~230 MB |
|
| | benchmark_100k | 100,000 | 100 | ~2.3 GB |
|
| | benchmark_1M | 1,000,000 | 100 | ~23 GB |
|
| | benchmark_10M | 10,534,536 | 1000 | ~240 GB |
|
|
|
| The structure of the files is the same throughout:
|
|
|
| ```
|
| Schema([('parent_asin', String), # the id
|
| ('main_category', String),
|
| ('title', String),
|
| ('average_rating', Float64),
|
| ('rating_number', Float64),
|
| ('description', String),
|
| ('price', Float64),
|
| ('categories', String),
|
| ('image_url', String)])
|
| ('value', List(Float64)), # the vectors
|
| ```
|
|
|
| ## Data Access
|
|
|
| The product metadata and vectors are available using [HF Datasets](https://huggingface.co/docs/datasets/en/index).
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| benchmark_10k = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-10k")
|
| benchmark_100k = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-100k")
|
| benchmark_1M = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-1M")
|
| benchmark_10M = load_dataset("superlinked/external-benchmarking", data_dir="benchmark-10M")
|
| ```
|
|
|
| ## Dataset Production
|
|
|
| ### Source Data
|
| - **Origin**: [Amazon Reviews 2023 dataset](https://amazon-reviews-2023.github.io/)
|
| - **Categories**: `["Books", "Automotive", "Tools and Home Improvement", "All Beauty", "Electronics", "Software", "Health and Household"]`
|
|
|
| ### Embeddings
|
|
|
| The embeddings are created via a [superlinked config](https://github.com/superlinked/external-benchmarks/tree/main/superlinked_app). The resulting 4154 dim vector contains:
|
| - 1 categorical,
|
| - 3 number,
|
| - 3 text (`Qwen/Qwen3-Embedding-0.6B`),
|
| - and 1 image (`laion/CLIP-ViT-H-14-laion2B-s32B-b79K`)
|
|
|
| embeddings concatenated. |
