Gabriel Ita

Key Skills

Software

Appen

Top Subject Matter

AI language models

general knowledge

Natural Language Processing (NLP)

Top Data Types

Text

Document

Top Task Types

Text Generation

Prompt + Response Writing (SFT)

Entity (NER) Classification

Freelancer Overview

AI Data Trainer. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Appen. Education includes Bachelor of Science, Federal University Lafia (2021). AI-training focus includes data types such as Text and labeling workflows including Prompt + Response Writing (SFT), Evaluation, and Rating.

Labeling Experience

AI Data Trainer

AppenTextPrompt Response Writing SFT

As an AI Data Trainer, I generated and refined high-quality written responses to train and enhance AI language models. I evaluated AI-generated text outputs for clarity, factual accuracy, logical consistency, and compliance with detailed guidelines. My work involved large-scale annotation and ongoing improvement of language models through detailed feedback and revision cycles. • Applied structured guidelines to ensure annotation quality • Collaborated with QA teams for response improvement • Identified and corrected errors in AI outputs • Maintained high acceptance rates across annotation assignments

As an AI Data Trainer, I generated and refined high-quality written responses to train and enhance AI language models. I evaluated AI-generated text outputs for clarity, factual accuracy, logical consistency, and compliance with detailed guidelines. My work involved large-scale annotation and ongoing improvement of language models through detailed feedback and revision cycles. • Applied structured guidelines to ensure annotation quality • Collaborated with QA teams for response improvement • Identified and corrected errors in AI outputs • Maintained high acceptance rates across annotation assignments

2024 - Present

AI Evaluation/Rater (Project-based)

AppenText

I evaluated and rated AI-generated responses and content for logical reasoning, factual accuracy, bias, safety, and adherence to style guidelines. My responsibilities included reviewing annotated data and providing analytical insights to improve model responses. This work contributed to enhancing AI systems' accuracy, reliability, and reasoning abilities. • Rated AI outputs across large datasets • Delivered structured feedback on errors and improvement areas • Synthesized research findings into concise reports • Ensured guideline compliance throughout evaluation tasks

I evaluated and rated AI-generated responses and content for logical reasoning, factual accuracy, bias, safety, and adherence to style guidelines. My responsibilities included reviewing annotated data and providing analytical insights to improve model responses. This work contributed to enhancing AI systems' accuracy, reliability, and reasoning abilities. • Rated AI outputs across large datasets • Delivered structured feedback on errors and improvement areas • Synthesized research findings into concise reports • Ensured guideline compliance throughout evaluation tasks

2023 - 2024

Data Annotator – NLP

AppenTextEntity Ner Classification

I annotated and labeled datasets for Natural Language Processing (NLP) tasks, focusing on entity recognition and structured data classification. The work involved tagging entities and categories within text to facilitate model training. I ensured consistency and compliance by following strict annotation guidelines and quality criteria. • Tagged entities within unstructured text datasets • Classified and labeled samples for supervised training • Ensured accurate entity boundaries and attributes • Contributed to building high-quality NLP datasets

I annotated and labeled datasets for Natural Language Processing (NLP) tasks, focusing on entity recognition and structured data classification. The work involved tagging entities and categories within text to facilitate model training. I ensured consistency and compliance by following strict annotation guidelines and quality criteria. • Tagged entities within unstructured text datasets • Classified and labeled samples for supervised training • Ensured accurate entity boundaries and attributes • Contributed to building high-quality NLP datasets

2023 - 2023

Education

Work History