How do you measure accuracy?

Accuracy measurement is always defined with the client and based on the individual use case. With that being said, the majority of tasks are assessed using Percentage Agreement techniques with overlap. That means that there needs to be a statistically significant consensus on the correct answer. If this is not reached immediately, more labelers are brought in to reach the SLA threshold. Keep in mind that this approach is rendered much more efficient and cost-effective by Toloka’s proprietary task decomposition framework, which ensures quality is as high as possible without creating unneeded expense. )

Why should I use a managed service for my data labeling?

Having a Company whose only focus for 10+ years has been on enterprise-ready data labeling ensures you get the best possible expertise and efficiency while your team learns best practices. Scale up your operations keeping the quality as high as your team needs without needing to learn by making expensive mistakes. )

Why shouldn’t I just label with GPT or other LLMs?

Labeling with large language models grants you lower quality than a hybrid approach, and increases the chance of introducing unrealistic information paired with biases and ethical problems. These are mitigated when pairing LLM-generated labels with human signals from a crowd from over 100 countries, ensuring Toloka works for you and your data 24/7 across all data types (including Text, Visual, Web-based, or their combination) and 40+ languages. )

Can I have a hybrid pipeline use my own synthetic labels paired with Toloka’s human input?

In most cases yes. Toloka Hybrid Labeling can be hybrid also when it comes to the origin of the labels. As a company, we offer a variety of enterprise-tested LLMs fine-tuned specifically towards data labeling and a seamless pipeline for them, but we understand that you might have some specific needs or requirements for which you might not want to or be able to use them. )

Elevate the Value of
Your Data Annotation

Try Toloka Hybrid Labeling for high-quality, fast data annotation in over 40 languages—boosting your team's efficiency and ROI through a blend of LLM+human expertise.Try Toloka Hybrid Labeling for high-quality,
fast data annotation in over 40 languages—boosting
your team's efficiency and ROI through a blend of
LLM+human expertise.Try Toloka Hybrid Labeling for high-quality, fast data annotation in over 40 languages—boosting
your team's efficiency and ROI through a blend of LLM+human expertise.

Solve my data labeling problems

96%of Data Science practitioners mention Data Preparation and Data Cleaning among their most time-consuming tasks

State of Data Science Report 2023, Anaconda

The new frontier of enterprise-ready data annotation

Fine-tuned LLMs

Provide speed of execution and base quality

Human signals

Augment the LLM labels to reach the highest possible quality

Fully managed for you by Toloka engineers

Included in Gartner's 2023 Hype Cycle for Generative AI report

Can LLMs be trusted for your annotation?

Ambiguous tasks

Task type: Search relevance
Issue: Ambiguity in “reasonably well” type of instructions
Reason: LLMs often have different logic from humans

Outdated pre-training dataset

Task type: Product similarity
Issue: New products or new brands
Reason: LLMs hallucinate about things they were not trained on

LLMs incorrectly interpret slang and shorthand

Task type: Data cleaning
Issue: Typo correction
Reason: LLMs aren`t trained to understand all languages equally well

Yes, with human supervision

Get complex tasks decomposed to simple ones by our team to have them labeled by the most suited level of the pyramid.
LLM only, human only, or a mix of both to get up to 130,000 labels/day of the highest quality.

Data Labeling for NLP

Get high-quality training data developed specifically to match the needs of your Natural Language Processing (NLP) engine. With Toloka Hybrid Labeling you can get annotations for Named Entity Recognition (NER), Sentiment Analysis, Speech Recognition, Text and Intent Classification, Audio transcription, Text recognition, and much more.

Data Labeling for Computer Vision

Label images and videos and maximize the potential and efficiency of training data. Toloka Hybrid Labeling supports annotation for image classification, semantic segmentation, object detection and recognition, and instance segmentation. Labeling tools include bounding boxes, polygons and keypoint annotation.

Up to

10×

faster than crowd-only labeling

Up to

95%

accuracy

40+

languages

130k

labels per day

Custom-made for your needs

Toloka Hybrid Lableing takes care of everything for you: from Prompt Engineering to Quality Control

From your project parameters...

Data type

Instructions

Quality

Speed

Budget

...to a tailor-made solution

Toloka Hybrid Labeling

"We've achieved a 9-percent increase in search quality. This, in turn, has boosted the search conversion rate."

Alexey Shevenkov, CTO, Hepsiburada

"Thanks to Toloka, we're able to run numerous data projects on a regular basis. What we gain is a dependable approach to data labeling."

Crowd Solutions Architect

GDPR, ISO 27001 and ISO 27701 compliant

Secure, private, and trusted by world-leading enterprises

API

Integrate easily and directly with native APIs

24/7 global support

Take advantage of an elite global team dedicated to make you succeed

Managed service

Our team of expert data engineers partners with you on every step of the journey

FAQ

Accuracy measurement is always defined with the client and based on the individual use case.

With that being said, the majority of tasks are assessed using Percentage Agreement techniques with overlap. That means that there needs to be a statistically significant consensus on the correct answer. If this is not reached immediately, more labelers are brought in to reach the SLA threshold. Keep in mind that this approach is rendered much more efficient and cost-effective by Toloka’s proprietary task decomposition framework, which ensures quality is as high as possible without creating unneeded expense.
Having a Company whose only focus for 10+ years has been on enterprise-ready data labeling ensures you get the best possible expertise and efficiency while your team learns best practices. Scale up your operations keeping the quality as high as your team needs without needing to learn by making expensive mistakes.
Labeling with large language models grants you lower quality than a hybrid approach, and increases the chance of introducing unrealistic information paired with biases and ethical problems.

These are mitigated when pairing LLM-generated labels with human signals from a crowd from over 100 countries, ensuring Toloka works for you and your data 24/7 across all data types (including Text, Visual, Web-based, or their combination) and 40+ languages.
In most cases yes. Toloka Hybrid Labeling can be hybrid also when it comes to the origin of the labels. As a company, we offer a variety of enterprise-tested LLMs fine-tuned specifically towards data labeling and a seamless pipeline for them, but we understand that you might have some specific needs or requirements for which you might not want to or be able to use them.

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Elevate Your Data Annotation with Toloka Hybrid Labeling