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.

Data Annotation

Designing Reward Models: Annotating Data for Policy Improvement

Language models require fine-tuning for maximum performance. Therefore, reward modeling is needed to guide the training of these systems so that their results match real-world goals. Data annotation is a key step in providing quality feedback. When annotators identify the strongest or weakest responses of the system, they provide data-driven

Computer Vision

Building Weak Labeling Frameworks: Faster Annotation with Imperfect Signals

These frameworks use multiple data points (datums) to develop a model faster, even in the presence of noise in the control signals. This approach speeds up the learning process and is in line with the trend in machine learning to focus on improving data quality rather than model complexity. Weak

Computer Vision

Building Text-to-Video Training Sets

This is a breakthrough in text-to-video datasets, allowing models to generate videos with high accuracy. Combining videos with accurate text captions is essential for training AI models. This allows models to create realistic scenes, convey motion dynamics, and integrate text cues into video content. This opens up new possibilities for

Machine Learning

Curating Retrieval Candidates: Annotating Passages for Knowledge-Augmented Models

Careful selection and annotation of excerpts for AI models improve the efficiency of language processing and information retrieval. Annotation makes knowledge models more accurate and useful. By labeling candidate searchers, we provide models with high-quality data, which improves natural language processing. Advances in machine learning and NLP are improving data

Data Annotation

Iterative Dataset Refinement: Constantly Improving Quality Over Time

In machine learning, data rarely remains unchanged. Data is constantly reviewed, errors are corrected, and refinements are added to make the models more accurate. For example, if the algorithm often confuses objects in images, experts review the markup and make adjustments. This approach allows us to gradually improve data quality

Machine learning

Creating Prompt Templates: Standardizing Annotation Instructions for LLMs

Prompt templates allow you to standardize the format and structure of instructions for annotators working with texts, images, or other types of data. Each template has clearly defined steps and requirements for performing annotations. To annotate texts, you can create a template that includes tasks for highlighting key terms, analyzing

Machine Learning

Annotating Features for Interpretability: Mapping Inputs to Model Decisions

As machine learning models evolve, the need for feature annotation is more pressing than ever. By mapping the input and output of a model, we can better understand the AI decisions that will lead to sophisticated AI systems. Identifying key features helps us understand how factors influence the model'