| | ---
|
| | 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. |
