What are various data for similarity Search performance

1. SQuAD (Stanford Question Answering Dataset)

Use Case: Test vector search accuracy for question-answer retrieval.

How to Use:

Preprocess the dataset to create embeddings for questions and answers.

Use questions as queries and measure the retrieval accuracy against their respective answers.

2. MS MARCO

Use Case: Benchmark performance for document or passage ranking tasks.

How to Use:

Use passages and queries provided in the dataset.

Generate embeddings and use queries to retrieve relevant passages.

3. Quora Question Pairs

Use Case: Test duplicate or paraphrased question detection.

How to Use:

Generate embeddings for questions.

Use one question as a query and check if its paraphrase is retrieved as the most similar result.

4. TREC Datasets

Use Case: Test retrieval systems for a variety of topics, including QA, passage retrieval, and entity matching.

How to Use:

Choose a task, create embeddings, and evaluate search performance.

5. Synthetic Dataset for Quick Testing

If you want something lightweight and simple to start:

from sklearn.datasets import make_blobs

import numpy as np

# Generate synthetic data

data, _ = make_blobs(n_samples=100, centers=5, n_features=50, random_state=42)

# Convert to strings for a mock dataset

docs = [" ".join(map(str, row)) for row in data]

# Example: Simulate a search query

query = np.mean(data[:10], axis=0)  # Take an average vector as query

1. Sentence Similarity Datasets:

STSBenchmark: A collection of sentence pairs with human-rated similarity scores. This dataset is widely used for evaluating sentence embedding models and similarity search.   

SemEval datasets: SemEval has hosted several tasks related to semantic similarity, including paraphrase identification and textual entailment, which can provide valuable data for evaluating vector similarity search.   

Quora Question Pairs: A dataset of question pairs from Quora, where the task is to determine whether a pair of questions are duplicates.   

2. Text Retrieval Datasets:

MS MARCO: A large-scale dataset for document ranking, containing passages from Wikipedia and a set of queries.   

TREC datasets: A collection of datasets for information retrieval tasks, including question answering and document retrieval. Some TREC datasets can be adapted for vector similarity search evaluation.   

News2Dataset: A dataset of news articles and their corresponding summaries, which can be used to evaluate the retrieval of relevant documents based on query vectors.

3. Image Retrieval Datasets:

ImageNet: A large image dataset with millions of images and associated labels. It can be used to evaluate image similarity search by comparing query images to images in the dataset.   

Places365: A dataset of images categorized into 365 scene categories, which can be used to evaluate place recognition and image retrieval based on visual similarity.   

4. Code Search Datasets:

GitHub CodeSearchNet: A large dataset of code snippets and their natural language descriptions, which can be used to evaluate code search based on textual queries.   

Key Considerations When Choosing a Dataset:

Relevance to your use case: Select a dataset that is relevant to the specific application of vector similarity search you are interested in (e.g., question answering, product recommendation, image search).

Dataset size: Choose a dataset that is large enough to provide a meaningful evaluation of your system's performance.

Data quality: Ensure that the dataset is of high quality and free from errors or biases.

Availability and licensing: Make sure that the dataset is readily available and that you have the necessary rights to use it for your evaluation.

By using these datasets, you can effectively test the performance of your vector store and compare different approaches to vector similarity search.

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