Data labeling 
for natural language 

Leverage human insight to extract information from natural language data.
Power your NLP algorithms with datasets of any size.

Get more out of your NLP training data with human annotation

Natural language processing (NLP) requires vast amounts of data to train AI to interpret human language. But data quality is just as important as quantity. 
NLP training data with human insights can improve the accuracy, robustness, and interpretability of your NLP models. 
With Toloka, you can build a predictable pipeline of high-quality training data that impacts your NLP algorithms.

Annotations we support

Toloka handles almost any input data for NLP data labeling: text, audio, image, or video. Our platform supports data annotation for named entity recognition, sentiment analysis, speech recognition, text and intent classification, text recognition, and more.

Why Toloka

  • ML technologies
    • One platform to manage human labeling & ML
    • Prebuilt scalable infrastructure for training and real-time inference
    • Flexible foundation models pre-trained on large datasets
    • Automatic retraining and monitoring out of the box
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  • Diverse global crowd
    • 100+ countries
    • 40+ languages
    • 200k+ monthly active Tolokers
    • 800+ daily active projects
    • 24/7 continuous data labeling
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  • Crowdsourcing technologies
    • Advanced quality control and adaptive crowd selection
    • Smart matching mechanisms
    • 10 years of industry experience and proven methodology
    • Open-source Python library for aggregation methods
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  • Robust secure infrastructure
    • Privacy-first, GDPR-compliant focus on data protection test
    • ISO 27001-certified
    • Multiple data storage options, Microsoft Azure cloud
    • Automatic scaling to handle any volumes
    • API and open-source libraries for seamless integration
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For developers

  • API
    Our open API gives you the freedom 
    to integrate directly into any pipelines
  • Python SDK
    Our Python toolkit covers all API 
    functionality to give you the full 
    power of Toloka
  • Java SDK
    Our Java client library provides a lightweight 
    interface to the Toloka API that works 
    in any Java environment


  • You can use self-service data labeling tools on the Toloka platform to get high-quality human-labeled data from the crowd. We also offer autolabeling with ML models and human-in-the-loop feedback for training machine learning algorithms. In some scenarios, algorithms label most of the data and send only those labels with low confidence for human verification.Contact us to find the ideal solution for your data labeling process.
  • Toloka gives you the platform and tools to manage the data labeling process instead of managing people. By implementing state-of-the-art technologies based on years of research and experimentation, we achieve reliable data quality from our huge crowd of Tolokers.If you're looking for a fully managed solution or you prefer to use your own in-house data labeling team, reach out to discuss your project needs.
  • You can send raw data to Toloka for data annotation to create your training dataset. You can also use the platform to collect new data from the Toloka crowd, such as written or spoken utterances in over 40 languages.If you don't have a large supply of data available, try our Adaptive AutoML. Because our models are pre-trained on huge datasets, you can quickly adapt them to your specific task by uploading a relatively small dataset for fine-tuning. The Toloka ML platform offers a range of pre-trained models for sentiment analysis, text classification, speech recognition, text generation, and other NLP projects in multiple languages.

Have an NLP project in mind?

Take advantage of Toloka technologies. Chat with an expert to learn how to get reliable training data for machine learning at any scale.