Ziyu H.

Key Skills

Software

Label Studio

Other

Top Subject Matter

Conversational AI

content moderation

information extraction

Top Data Types

Text

Top Task Types

Entity (NER) Classification

Freelancer Overview

Data Labeling Intern/Part-time. Core strengths include Label Studio and Other. Education includes Bachelor of Science, 中山大学 (2020). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification.

Labeling Experience

Project: Text Annotation & Evaluation Specification Exploration

OtherTextEntity (NER) Classification

Explored the impact of annotation granularity on model training effectiveness for varied text content. Designed multi-version annotation specifications, conducted comparative experiments, and produced reusable evaluation strategies and reports. Utilized data processing scripts and statistical analysis for benchmarking annotations. • Developed adaptable annotation strategies for model optimization • Performed controlled experiments on label granularity • Generated detailed evaluation and impact reports • Managed annotation and analysis workflow using Python scripts

2021 - 2021

Project: Dialogue System Data Annotation and Governance

Label StudioTextEntity (NER) Classification

Led the annotation and governance of dialogue datasets for a conversational AI project. Developed comprehensive annotation guides covering intent, slots, and dialogue states, and designed multi-level QC with third-party sampling and correction. Managed data desensitization and ensured privacy compliance in dataset outputs. • Produced high-quality labeled datasets for dialogue understanding and model fine-tuning • Established and maintained scalable annotation workflows • Integrated error correction and version rollback mechanisms • Utilized Label Studio, Python, and spreadsheet tools for annotation management

2020 - 2021

Data Labeling Intern/Part-time

Label StudioTextEntity (NER) Classification

Served as a data annotator and quality control specialist for text and dialogue data in AI systems. Designed and implemented annotation guidelines for intent, slot filling, named entities, and content moderation tasks. Conducted multi-stage quality control, error correction, data cleaning, and privacy protection. • Designed guidelines for dialogue, intent, and entity recognition • Built and maintained multi-phase QC process (initial annotation, review, audit) • Ensured data desensitization and privacy compliance • Leveraged tools such as Label Studio, Prodigy, and proprietary platforms

2019 - 2020

Education

Work History