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Han Sern Fong

Han Sern Fong

Expert Data Annotator

USA flagKalamazoo, Usa
$15.00/hrIntermediateOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

ImageImage
TextText
VideoVideo

Top Task Types

No task types listed

Freelancer Overview

I have years of experience building quality training data tailored to machine learning models from scratch. I have experience annotating raw sets of data in myriad different modes (text/image/audio) using the full pipeline, writing up detailed annotation rules, doing quality assurance, and overseeing a labeling team. I have experience working on complex projects that require depth of understanding involving sentiment analysis, intent classification, semantic segmentation for computer vision, and named entity recognition for large language models (LLMs). What I am good at My strengths are a very comprehensive, context-based method in which integrity and consistency of data is of the utmost importance. I know how to use a variety of good-quality labelers from industry standards and am expert in creating and tuning annotation taxonomies and instructions that reduce ambiguity for labelers and improve performance of models. This hands-on working alongside strategic monitoring means that I can play an active role in the creation of AI systems that are more precise, trustworthy, and are ethical.

IntermediateMalayEnglishChinese Mandarin

Labeling Experience

Generalist

OtherImageBounding BoxSegmentation
This project has many different tasks and below are some examples. AI Response Evaluation – Evaluating AI answers for correctness, relevance, reasoning quality, and completeness. Preference Ranking – Analyzing several AI responses to choose the best one based on project guidelines. Fact-Checking & Hallucination Detection – Detecting incorrect, misleading, or fabricated information. Instruction Following Assessment – Verifying whether the model correctly followed user prompts and constraints. Reasoning & Logic Evaluation – Evaluation of step-by-step reasoning, coherence, and accuracy in problem-solving. Tone & Safety Review – Verifying that answers are appropriate, unbiased, safe, and policy-compliant. Text Quality Review – Assessing the readability, grammar, structure, and clarity of AI outputs. Error Classification – Identifying whether a certain error is factual, logical, formatting, or instruction-related. Feedback & Justification Writing – Explaining rankings and correction

This project has many different tasks and below are some examples. AI Response Evaluation – Evaluating AI answers for correctness, relevance, reasoning quality, and completeness. Preference Ranking – Analyzing several AI responses to choose the best one based on project guidelines. Fact-Checking & Hallucination Detection – Detecting incorrect, misleading, or fabricated information. Instruction Following Assessment – Verifying whether the model correctly followed user prompts and constraints. Reasoning & Logic Evaluation – Evaluation of step-by-step reasoning, coherence, and accuracy in problem-solving. Tone & Safety Review – Verifying that answers are appropriate, unbiased, safe, and policy-compliant. Text Quality Review – Assessing the readability, grammar, structure, and clarity of AI outputs. Error Classification – Identifying whether a certain error is factual, logical, formatting, or instruction-related. Feedback & Justification Writing – Explaining rankings and correction

2025 - 2025

Education

W

Western Michigan University, Haworth College of Business

Bachelor of Business Administration, Computer Information Systems

Bachelor of Business Administration
Not specified

Work History

A

Adaptalytics

Data Scientist/Consultant

Remote
2024 - Present
S

Stryker

Data Analyst

Portage
2023 - 2024