Data annotation & labeling blog.

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Data annotation

Data annotation

Data annotation is one of the important parts of machine learning, especially in AI example. To know more about this and get better knowledge on Data Annotation, we need to know ALL the basics! So, we will be putting up a blog on "Data Annotation" [AIN-103] with A - Z coverage which will help you in understanding different parts related to Annotation of data.

Computer Vision

Gamifying Annotation: Increasing Labeler Engagement and Accuracy

Gamification involves adding game-like elements, such as points, rewards, levels, leaderboards, and challenges, to encourage participation and enhance motivation. Integrating these features may make the process more enjoyable and rewarding for labelers, potentially leading to higher engagement and better focus. This can result in improved annotation quality and faster processing

Data Annotation

Evidence Linking Annotation: Connecting Sources to Claims

When we talk about AI-based computer models, an essential component of their work is correctly connecting data with their sources. Evidence-based annotation is a process that allows you to automatically or semi-automatically link the information presented in a study to reliable sources. This approach adds confidence in the accuracy of

Computer Vision

Selective Subset Labeling: Building Targeted Benchmarks Within Large Corpora

Selective subset labeling is a method of creating benchmarks in large data sets. Companies can optimize machine learning models by focusing on subsets of data. This method ensures accuracy and reduces the time and resources required to annotate data. Key Takeaways * Selective subset labeling is a method for optimizing machine

Machine Learning

Adaptive Prompting: Dynamic Instructions Based on Model Feedback

Adaptive prompts are the key to correct responses across NLP tasks. This method has changed the way large language models interact and train. Language models generate responses based on feedback, which is important for high-quality task performance with multi-step reasoning. The integration and development of adaptive methods ensure the performance

Data Labeling

Customizing Label Studio: Tailored Interfaces for Complex Annotation Tasks

Label Studio stands out among other annotation tools due to its versatility. It supports work with texts, images, audio, and video, making it indispensable for scientists and machine learning experts. Its flexibility allows you to optimize workflows and improve the quality of annotation. Thanks to the personalization of interfaces, you

Data Annotation

Safety Benchmarking: Annotating Data to Detect Harmful Content

As language models become more sophisticated, they must be monitored for their responses to harmful behavior. Established safety assessments provide a basis for assessing these risks and ensure that AI models comply with ethical standards. The foundation of practical evaluation is high-quality annotated data. This uses automated tools and human

Computer Vision

Curriculum Labeling: Structuring Data to Teach Models in Stages

This approach is changing the way AI models are trained. It offers structured methods to improve the accuracy of AI models. Curriculum labeling is a method in semi-supervised learning that structures data annotations to optimize AI training results. Unlike traditional data labeling, curriculum labeling reruns the model parameters before each