Pricing

Pay per event

🧽 Dataset Deduplicator - Clean Any Scraped Dataset

🧽 Upload any dataset — scraped rows, a RAG corpus, an export — and get back exact AND near-duplicates removed, plus a per-field data quality report. ✅ MinHash + LSH banding finds near-duplicate text at scale without O(n²) pairwise comparison.

Pricing

Pay per event

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Developer

mohamed alaya

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Monthly active users

5 days ago

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Categories

Developer tools

You can access the 🧽 Dataset Deduplicator - Clean Any Scraped Dataset programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1import { ApifyClient } from 'apify-client';
2
3// Initialize the ApifyClient with your Apify API token
4// Replace the '<YOUR_API_TOKEN>' with your token
5const client = new ApifyClient({
6    token: '<YOUR_API_TOKEN>',
7});
8
9// Prepare Actor input
10const input = {
11    "records": [
12        {
13            "title": "Best wireless headphones 2024",
14            "body": "These wireless headphones deliver excellent sound quality and battery life for the price."
15        },
16        {
17            "title": "Best wireless headphones 2024",
18            "body": "These wireless headphones deliver excellent sound quality and battery life for the price."
19        },
20        {
21            "title": "Best wireless headphones of 2024",
22            "body": "These wireless headphones deliver great sound quality and long battery life for the price."
23        },
24        {
25            "title": "Top budget laptops this year",
26            "body": "A roundup of the best budget laptops you can buy right now, covering performance and battery."
27        }
28    ]
29};
30
31// Run the Actor and wait for it to finish
32const run = await client.actor("that_red_bird/dataset-deduplicator").call(input);
33
34// Fetch and print Actor results from the run's dataset (if any)
35console.log('Results from dataset');
36console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
37const { items } = await client.dataset(run.defaultDatasetId).listItems();
38items.forEach((item) => {
39    console.dir(item);
40});
41
42// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

🧽 Dataset Deduplicator - Clean Any Scraped Dataset API in JavaScript

The Apify API client for JavaScript is the official library that allows you to use 🧽 Dataset Deduplicator - Clean Any Scraped Dataset API in JavaScript or TypeScript, providing convenience functions and automatic retries on errors.

Install the apify-client

$npm install apify-client

Other API clients include:

🧽 Dataset Deduplicator - Clean Any Scraped Dataset API in Python

🧽 Dataset Deduplicator - Clean Any Scraped Dataset API through CLI

🧽 Dataset Deduplicator - Clean Any Scraped Dataset OpenAPI definition

🧽 Dataset Deduplicator - Clean Any Scraped Dataset API

Text Dedupe — exact & near-duplicate detection, Japanese ready

shoebill-dev27/text-dedupe

Detect exact and near-duplicate texts in scraped or collected datasets: normalized hashing for exact matches plus character-shingle Jaccard similarity for near matches. Works on Japanese (no word tokenization needed). Returns per-text verdicts and duplicate clusters. No LLM cost.

Shinobu Otani

Dataset Deduplicator & Cleaner

eszetael_lab/dataset-deduplicator-cleaner

Returns your records with duplicates removed and values normalized — same fields as the input, nothing added or renamed. Exact, normalized or fuzzy matching on keys you choose. A data quality report lands under QUALITY_REPORT. Takes a dataset ID or inline JSON, so it chains after any scraper.

Radosław Szal

Dataset Deduper | Exact & Fuzzy Dedupe for Any Dataset

broomwagon/dataset-deduper

Collapse duplicates in any dataset, in two passes you control. Exact matching ignores case, punctuation, and word order; an optional fuzzy pass catches the near-duplicates normalization cannot. Every decision is explained in an audit trail, and a cross-run ledger remembers what you already got.

Brandon Mensing

Sitemap to RAG Dataset

ghostgrid/sitemap-to-rag-dataset

Convert any website sitemap into an LLM-ready dataset for RAG pipelines.

GhostGrid

Dataset Deduplicator v2

zentrafoundry/dataset-deduplicator-v2

Transform dataset deduplicator v2 inputs into structured rows, clear errors, confidence signals, and automation-ready output.

Zentra

Scraped Data Cleaner & Converter (No-Code CSV/JSON Tool) Rental

m3web/scraped-data-cleaner-rental

Clean and organize scraped .json or .csv data — no coding required. Remove duplicates, empty rows, unwanted columns, and sort by any field. Cleaned results are pushed to your Apify dataset. Perfect for marketers, researchers, and no-code workflows.

M3Web

5.0

🧩 RAG Dataset Builder - Crawl to Embedding-Ready Chunks

that_red_bird/rag-dataset-builder

🧩 Crawl a site and get back a FINISHED, embedding-ready RAG dataset — not just clean text. ✅ Semantic chunking on heading boundaries, never mid-code-block or mid-table. ✅ Token estimator, configurable overlap, near-duplicate removal, full provenance per chunk.

mohamed alaya

Data.gov.uk Scraper - Cheap 🌐📊🇬🇧

scrapestorm/data-gov-uk-scraper---cheap

🔎 Easily collect dataset listings from data.gov.uk Provide one or multiple search URLs and extract dataset information such as 📄 Dataset Title 🏢 Published By 🕒 Last Updated 📝 Description 🔗 Dataset URL & more Perfect for open data research, government data monitoring & dataset discovery 📊🚀

Storm_Scraper

5.0

Dataset Download

idiatech/apify-Dataset-Download

Download any dataset from the Apify platform automatically and in any format you want. Use this actor along with a Dataset toolbox automation tool.

idIA Tech

Dataset Toolkit — Dedupe, Merge, Clean, Diff

glueworks/dataset-toolkit

Post-process any Apify dataset: remove duplicates, merge multiple datasets, clean and normalize fields, or diff two crawls. Outputs a new dataset plus a summary record.