Get your text labeled by real people in the countries and languages you choose: sentiment, categories, names, relevance and translation checks. Set the task, set the pay and approve only the labels that fit.
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Launch a text labeling task yourself and start receiving labels from native speakers, without long negotiations.
Put a batch of texts into the task, list the labels workers can choose from and explain each one with an example.
Batch of texts per submissionYour own label listExample for every label
Select the countries your contributors should come from and the language of the texts. Native speakers catch meaning, slang and tone that others miss.
Any number of countries in one taskNative speakers by countryYou set the total volume
Your task becomes available to workers in the selected countries. Each person labels the batch once, so you can get several independent labels for the same texts.
Labels for every text in the batchIndependent opinionsNew person in every submission
Check every submission, approve the labels that follow your rules and reject the rest. Compare answers from several people to find the most reliable label.
Check every submissionApprove or reject each oneClean labels for trainingSet up a labeling task for the exact signal your language model needs. Contributors follow your rules and work in their own language.
Contributors read comments, messages or reviews and mark the sentiment and emotion. Native speakers understand sarcasm and slang that automatic tools miss.
Positive, negative, neutral or mixedEmotions you defineSarcasm and slang understood
Workers assign each text to one of your categories, such as topic, request type or urgency. Use the labels to train routing, search and moderation models.
Your own category listOne or several labels per textClear examples for each category
Contributors find and list people, companies, places, dates or products mentioned in each text. Train models to extract key information automatically.
Entity types you chooseLocal names and spellingsExact words copied from the text
Workers see a search query and a list of results and rate how well each one matches. Improve search, recommendations and retrieval for real users.
Query and results in the taskRating scale you defineLocal intent by country
Contributors compare a source text with its translation and mark errors, missing meaning or unnatural wording. Find weak spots in machine translation early.
Accuracy and fluency checksErrors marked and explainedNative speakers of the target language
Workers correct mistakes in texts you provide and rewrite unclear sentences. Collect before and after pairs for correction and editing models.
Original and corrected versionsSpelling and grammar fixesNatural local wording
Put a batch of texts into one task. Every worker labels the whole batch once, and you collect several independent labels for each text.
Your setup
Choose Text collection, paste a numbered batch of texts into the instructions and list the labels workers can use.
Text collection task typeNumbered batch of textsLabel list with examplesYour view
Each submission arrives with a label for every text in the batch, ready to review and compare with other workers' answers.
Labels for the full batchWorker country on every submissionApprove or reject each setChoose where your labelers come from and which language they work in. Every submission comes from a different person.
Accept workers from all countries or pick specific ones by continent or individually. Get labels from people who use the language every day.
Describe who should take part in your instructions, such as language level or familiarity with a topic. Reject submissions that do not match.
Add an example for every label so contributors apply your rules the same way and your data stays consistent.
Each worker labels your batch only once. Order several submissions per batch to compare answers and keep the most reliable label.
You decide what counts as a correct label and review every submission before it joins your dataset.
Define every label with a short description and an example.
Add a few texts with an obvious correct label to spot careless submissions.
Check each submission and accept only labels that follow your rules.
Compare labels from several workers on the same texts and keep the ones they agree on.
Every label comes from a real person who chose to take your task and knew what it was for.
Your task description explains what people will label and that the results will be used to train AI.
Nobody is assigned to your task. Each worker decides to take it and submits their labels on their own.
Each submission comes from a specific worker in the country you selected, completed specifically for your task.
You set the pay per task. One submission is one labeled batch. Adjust the numbers to see what your project will cost, with the 15% platform fee included.
How long it takes a worker to complete one task.
Minimum pay is $0.10 per task. Need more labels later? Extend the same task instead of creating a new one.
NLP training data is text with human labels that teach language models to understand meaning: sentiment, topics, names, relevance and correct wording. Labels from native speakers make models work better for real users.
Here workers label text you already have. To get new text written by people, see text data collection. To get feedback on your model's answers, see LLM training data.
Put a batch of texts into one task, for example 20 numbered comments. Each worker labels the whole batch in one submission. For more texts, create several tasks with different batches.
For simple labels one or two opinions may be enough. For subjective labels like sentiment, order three or more submissions per batch and keep the label most people agree on.
Any language spoken by workers in the countries you select. Choose the countries and state the language in your instructions.
You set the pay for each task, starting from $0.10, plus a 15% platform fee. Bigger batches and harder labels need a higher pay rate. A campaign starts from $0.30.
Paste a numbered list of texts into the task instructions. Keep each batch short enough to label in a few minutes and remove any personal data first.
You review every submission yourself and approve or reject it. Add control texts with known labels and compare answers from several workers to find careless work.
Yes. Extend your existing task instead of creating a new one. Workers who already took part still cannot submit again.
If you need larger batches, a special format or help designing your label rules, contact us through the form below and we will set it up with you.
Need many languages, large text volumes or help designing your labels? Send a message and we will set it up with you.