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Hire Data Labeling Workers from $0.05

Data labeling service from $0.05 per item. Real people read your text, apply your taxonomy, and submit their label with the classified text — you approve every submission before paying.

Create TaskHow It Works
Your Guarantee: Consistent, Rule-Based Labels

Workers Label from Your Taxonomy — Not Their Interpretation

Workers apply the label set you define and submit the text they labeled alongside their decision. You read the label and the text before paying — no batch-approved results you cannot verify.

  • Your taxonomy, applied item by item

    Workers select only from the label categories you define. If a text does not fit any category, workers flag it as unclear rather than forcing a wrong label. Submissions that assign a label outside your taxonomy are rejected before payment

  • Labeled text and decision submitted before payment

    Every submission includes the text that was labeled and the assigned label. You verify the worker read the correct item and applied the correct logic — not a default response or copied previous submission — before releasing payment

  • Reject and replace uncertain or inconsistent labels

    If a worker assigns the wrong label or does not follow your decision rules, reject the submission at no cost. Add an "Unclear" option to your taxonomy — workers who flag ambiguous items honestly produce cleaner training data than workers who guess

Data Labeling Use Cases

What Are You Labeling?

Sentiment, intent, relevance, quality, translation, entity marking — each task type needs its own taxonomy structure and decision rules in your brief.

Sentiment Analysis Training Data

Problem
Training a sentiment model requires thousands of text examples labeled as positive, negative, or neutral — with the labeling calibrated to your domain. Generic pre-labeled datasets do not match the vocabulary, tone, or edge cases in product reviews, support tickets, or social posts for a specific vertical.
With RapidWorkers
Workers label your text items against your sentiment taxonomy. You define what "positive" means in your context, include examples, and workers apply your definition consistently item by item. Pay per labeled item — no minimum dataset size, no platform contract required.

Chatbot Intent Classification

Problem
Building a reliable intent classifier for a chatbot or virtual assistant requires labeled examples of user messages for each intent in your taxonomy. Collecting and labeling enough examples per intent to train a model is slow when done manually in-house.
With RapidWorkers
Workers read each user message and assign it to the intent category that best matches from your defined list. Include a decision rule and example for each intent. Workers flag messages that do not clearly match any intent, giving you a clean dataset rather than noise.

Search Relevance Rating

Problem
Search and recommendation systems require human relevance judgments to train learning-to-rank models and to evaluate retrieval quality. Generating enough query-result relevance labels in-house is expensive at the scale needed for meaningful model training.
With RapidWorkers
Workers rate how relevant each search result is to its query using your defined rating scale. You provide the query and result, workers provide the relevance judgment and a one-sentence reason. Collect hundreds of relevance labels per day at a fraction of the cost of enterprise annotation vendors.

Content Quality Assessment

Problem
Search engines, content platforms, and recommendation systems that rely on content quality signals need human raters to score article or listing quality — helpfulness, accuracy, originality, readability — before automated quality models can be trained or evaluated.
With RapidWorkers
Workers rate each piece of content against your quality rubric and submit their rating with a written reason. Define each quality dimension explicitly in your brief: what a "3" means vs. a "1" on each axis. Review every rating and reason before paying — outlier ratings are visible before they enter your dataset.

Translation Quality Review

Problem
Machine translation output quality varies significantly by language pair, domain, and source text complexity. Evaluating translation quality at scale requires bilingual raters who can compare source and target text — not just fluent speakers of the target language alone.
With RapidWorkers
Workers who are fluent in both the source and target language rate each translation for accuracy and fluency on your defined scale. Workers flag specific translation errors (wrong meaning, awkward phrasing, missing content) as part of their submission. Specify the language pair in your brief so workers without the required bilingual proficiency skip the task.

Text Span and Entity Marking

Problem
Named entity recognition (NER) models require examples of text with specific entity types — person names, organization names, locations, dates, product names — marked and labeled within context. Manual NER annotation at scale is slow without a structured task format.
With RapidWorkers
Workers copy and paste the relevant text span from a short snippet and label it with the entity type from your defined list. One entity marking per task keeps the work structured and the output traceable. Use for bootstrapping small NER datasets, evaluating model output, or generating examples for specific entity types your existing model misses.
How It Works

How to Hire Data Labeling Workers

Write your taxonomy and submit text items. Workers label one by one — you approve every label before paying.

01

Write Taxonomy and Rules

List every label with a decision rule, examples, and an Unclear option for items that do not fit.

02

Submit Your Text Items

Provide one text item per task. Keep metadata out of the brief so workers label only the text.

03

Workers Apply Your Labels

Workers read the item, choose from your taxonomy, and use Unclear when the text is ambiguous.

04

You Approve Label and Proof

Workers submit the label and labeled text. Reject wrong labels or submissions for the wrong item.

Pricing

Data Labeling Pricing

Cost depends on text length, number of label fields, and whether workers need to write a reason. A simple binary classification on a short text snippet starts at $0.05 — multi-field annotation with a written reason costs more.

Minimum Reward

$0.05

per completed task

Example Task Rewards

TaskExample Reward
Binary classification (spam/not spam, relevant/not relevant, short text)$0.05–$0.08
Multi-class text classification (one of 5–10 categories, short snippet)$0.08–$0.12
Multi-label annotation (apply all applicable labels, written reason required)$0.10–$0.15
Structured annotation (sentiment + topic + quality, 3+ fields per item)$0.12–$0.20

*These are examples, not fixed pricing tiers.

Calculate Your Cost

Estimate cost by field count and text length. One text item per task submission gives you clean, traceable labels — bundling multiple items per task produces lower-quality results.

Example: 5,000 tasks × $0.08 reward = $400 estimated total

Data Labeling Task

Calculator
Tasks5,000
Reward Per Task$0.08
Estimated Total5,000 × $0.08
$400
Create Task

Require workers to paste the labeled text in their submission alongside the assigned label — this confirms they read and labeled the correct item, not a placeholder response.

Example Task

What Does a Data Labeling Task Look Like?

rapidworkers.io/dashboard/job/data-labeling-example

Dashboard / Data Labeling Task

Label Customer Support Message — Sentiment, Topic, Urgency

Pay Rate

$0.12

Time to Complete

3 min

Availability

15 / 40

Requirement

English fluency

Job Description

Read the customer message. Fill in three fields (Sentiment, Topic, Urgency) from provided options. Use "Unclear" if you cannot determine a field. Paste the full message text. Submit.

Reference

Completed label fields alongside the pasted customer message text

Submission Instructions

Sentiment (Positive / Negative / Neutral)

Primary topic (Billing / Shipping / Product quality / Account access / Other)

Urgency (High / Medium / Low / Unclear)

Labeled text (paste the full message you classified)

Complete Job

This is how workers see your task on RapidWorkers

Job Description

Read the customer support message provided below and fill in three classification fields: (1) Sentiment — select one: Positive, Negative, Neutral; (2) Primary topic — select one: Billing, Shipping, Product quality, Account access, Other; (3) Urgency — select one: High (customer asking to cancel or threatening to leave), Medium (customer frustrated but not escalating), Low (general question or feedback with no urgency signal).

After filling in all three fields, paste the full text of the support message you classified in the "Labeled text" field. If you cannot determine one of the fields clearly from the message alone, select "Unclear" for that field and explain why in the notes field. Do not guess.

Submission Instructions

Step 1: Read the customer support message in the brief.

Step 2: Fill in all three classification fields from the provided options.

Step 3: Use "Unclear" for any field that cannot be determined from the text alone.

Step 4: Paste the full message text in the "Labeled text" field. Submit.

Create Your Task
Got Questions?

Frequently Asked Questions

Short text classification (sentences, product reviews, social media posts, support tickets, search queries), multi-label classification (apply multiple categories to a single text), relevance rating (is this search result relevant to this query?), sentiment annotation, and basic named entity marking (highlight and label names, organizations, or locations within a text snippet). Long documents should be broken into shorter passages before submitting — workers complete one item per task, not full-document annotation.

Add an explicit "Unclear / Cannot determine" option to your label set and instruct workers to use it rather than guess. Submissions that use "Unclear" flag the item for your review without requiring a payment decision. This produces more reliable training data than forcing a classification on an ambiguous item — uncertain labels contaminate model training more than missing labels do.

Yes. Specify the language of the text in your brief and require workers to be fluent in that language. Workers who cannot read the text language skip the task. Common languages (Spanish, Portuguese, French, German, Arabic, Indonesian, Hindi) have good worker pool coverage. For less common languages, availability is limited — test with a small batch before committing to a large volume.

Yes. Provide the user message to label and your intent taxonomy (list of possible intents with a definition for each). Workers read the message and assign the closest intent. For ambiguous messages that could match multiple intents, require workers to select the primary intent and flag the message as ambiguous. Keep your intent taxonomy to 10–15 labels maximum — more categories increase per-item review time and inconsistency.

Yes. Define your rating scale explicitly in the brief (e.g., 1 = not relevant, 2 = partially relevant, 3 = highly relevant) and include a concrete example for each rating level. Workers rate each item and provide a one-sentence reason for their rating. Anchor your scale with examples — "1 means..." and "3 means..." — otherwise workers calibrate the middle values differently.

Workers can manually copy and paste or quote the relevant span from the text and label it. This works for short text snippets where the entity occurs once or twice. For complex multi-entity annotation in long documents, or for tasks requiring bounding boxes on text (like form field extraction), RapidWorkers is not the right tool — specialist annotation platforms with integrated markup interfaces are needed.

Write your label definitions with decision rules, not just names. "Positive" is not a definition — "Positive: the overall tone is favorable toward the product, even if one aspect is criticized" is. Include at least two examples per label, and one counter-example per label that shows what it is NOT. After the first 30–50 submissions, run a consistency check: look for labels that vary on the same type of item, then tighten that part of the definition.

Ready to Label Your Training Dataset?

Write your taxonomy and decision rules, post your text items, and let real workers apply your labels — you review every submission before releasing payment.

Create Task
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