Dinda Alayda Ridwan

Key Skills

Software

Mindrift

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Text

Top Task Types

Classification

Data Collection

Evaluation Rating

Mapping

Text Summarization

Freelancer Overview

Although I’m new to the world of AI training and data labeling, I bring strong motivation, a keen interest in technology, and a passion for learning. I’m eager to dive into the intricacies of data preparation, labeling, and AI model training, and I am committed to building the skills necessary for success in this field. I have an analytical mindset and a problem-solving attitude, which I believe will serve me well as I start working with various types of datasets and AI tools. I’m excited to start learning the tools and techniques needed for data labeling, including classification tasks and annotation workflows, and I’m ready to contribute wherever needed. With a proactive approach to self-learning, I’m actively building my understanding of AI and data science through online courses and hands-on projects, and I’m eager to gain practical experience by collaborating on real-world AI projects.

Labeling Experience

AI Annotator

MindriftTextMappingEvaluation Rating

1. Mapping: Ensure clarity in Option Key mapping to minimize ambiguity, reduce QA rejections, and maintain alignment with the client’s taxonomy. These guidelines apply to all items, regardless of whether a specific attribute is displayed on the category page. 2. Evaluation/Rating: Reframe the previous rationales using a new scoring rubric. Write a rationale for the Overall Quality rating for a single response task and choice between the two models for a pairwise task. 3. Data Collection: Analyze the merchant's collection data to determine whether it is taxonomy-related, identify any gaps between the merchant's intent and the standardized attributes, and assess the overall match quality.

1. Mapping: Ensure clarity in Option Key mapping to minimize ambiguity, reduce QA rejections, and maintain alignment with the client’s taxonomy. These guidelines apply to all items, regardless of whether a specific attribute is displayed on the category page. 2. Evaluation/Rating: Reframe the previous rationales using a new scoring rubric. Write a rationale for the Overall Quality rating for a single response task and choice between the two models for a pairwise task. 3. Data Collection: Analyze the merchant's collection data to determine whether it is taxonomy-related, identify any gaps between the merchant's intent and the standardized attributes, and assess the overall match quality.

2025

Education

Work History