Annotations we support
Use the Toloka crowd to evaluate the performance of your search engine and discover which ranking model works best. Collect data for improving your search relevance algorithm.
Use cases:Ask performers to classify or categorize entire texts with predefined category tags.
Use cases:Use Toloka to label texts with sentiment categories for any purpose, from understanding customer reviews to spam filtering.
Use cases:Ask performers to categorize user queries into relevant predefined intents. Use labeled data to train your chatbot, voice assistant, or any other conversational agent to better understand your users.
Use cases:Create a collection of utterances that typically occur in conversations, based on instructions or scenarios that you provide for our performers.
Use cases:Use our skilled performers to identify parts of text, classify proper nouns, or label any other entities.
Use cases:Get recorded speech samples from performers according to your instructions and use them to create or fine-tune a voice interface.
Use cases:Ask performers to transcribe audio files or check existing transcriptions for accuracy.
Use cases:Use Toloka to detect emotion, categorize topics, or identify events in audio samples or conversations to improve your model.
Use cases:Ask performers to transcribe text in PDF files. Use labeled data to train your text recognition algorithms to better identify specific parts of scanned documents, or validate and fine-tune the output of your own OCR models.
Use cases:Use the Toloka crowd to evaluate the performance of your search engine and discover which ranking model works best. Collect data for improving your search relevance algorithm.
Use cases:
Success stories
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
"Toloka helped us tackle the challenge of recognizing handwritten text in documents. We send anonymous text fragments to Toloka, where qualified performers rewrite the text and send the results back to the Dbrain system for further AI training. This meant we were able to resolve even the most difficult recognition cases for our customers."
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
"Modern machine learning technologies are not possible without a large volume of training examples. A computer can't master a natural language unless a person „explains“ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter."
Angelina Galkevich, Analyst
Tinkoff:
"We use Toloka to annotate data for speech synthesis and recognition. Toloka performers have already processed tens of thousands of tasks with audio files for us, and we have successfully used the results to train our models. We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different markup methods and hypotheses, and then choose the optimal approach."
Anton Slesarev, head of Technologies in the self-driving cars division at Yandex:
"Toloka is my favorite Yandex service and the first place we go to prepare data for AI. To train the neural network, we need tens of thousands of annotated images. You can buy them for $4 each, or you can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms. We use these datasets for developing self-driving vehicle technology."
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
"Toloka helped us tackle the challenge of recognizing handwritten text in documents. We send anonymous text fragments to Toloka, where qualified performers rewrite the text and send the results back to the Dbrain system for further AI training. This meant we were able to resolve even the most difficult recognition cases for our customers."
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
"Modern machine learning technologies are not possible without a large volume of training examples. A computer can't master a natural language unless a person „explains“ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter."
Angelina Galkevich, Analyst
Tinkoff:
"We use Toloka to annotate data for speech synthesis and recognition. Toloka performers have already processed tens of thousands of tasks with audio files for us, and we have successfully used the results to train our models. We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different markup methods and hypotheses, and then choose the optimal approach."
Anton Slesarev, head of Technologies in the self-driving cars division at Yandex:
"Toloka is my favorite Yandex service and the first place we go to prepare data for AI. To train the neural network, we need tens of thousands of annotated images. You can buy them for $4 each, or you can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms. We use these datasets for developing self-driving vehicle technology."