Chrispas Okioma

Key Skills

Software

Datasaur

LabelImg

Mercor

Mindrift

Remotasks

Roboflow

Toloka

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Top Task Types

Classification

Object Detection

Polygon

Prompt Response Writing SFT

Text Summarization

Freelancer Overview

I am an experienced data labeling and AI training contributor with a strong background in annotating and curating datasets for natural language processing (NLP), computer vision, and digital content analysis. My expertise includes text classification, sentiment labeling, entity recognition, and image/object detection using platforms such as Labelbox, CVAT, and Amazon SageMaker Ground Truth. I am skilled in developing and following detailed annotation guidelines, maintaining high inter-annotator agreement, and ensuring consistency across large datasets. In addition to technical labeling skills, I bring a research-informed understanding of digital communication, privacy, and online identity—insights that enhance my ability to interpret and label complex social, linguistic, and behavioral data accurately. My attention to detail, quality assurance experience, and familiarity with AI model evaluation make me well-equipped to contribute to high-quality training data for ethical and effective AI systems.

Labeling Experience

Multimodal AI Training Dataset – Text, Image, and Audio Annotation

DatasaurImageEntity Ner ClassificationClassification

Contributed to a large-scale, multimodal AI training initiative focused on improving the accuracy of natural language understanding, image recognition, and speech emotion detection systems. For text data, labeled and categorized over 15,000 samples from chat transcripts and social media posts for sentiment analysis, intent detection, and named entity recognition (NER). For image datasets, annotated thousands of objects using bounding boxes and segmentation tools to support object detection in urban and retail environments. Additionally, labeled audio clips for emotion and speaker recognition tasks in multilingual datasets (English and Spanish). Ensured data quality through double-review validation, inter-annotator agreement (IAA) checks exceeding 95%, and adherence to project-specific ontology and annotation guidelines. Collaborated with QA teams to refine taxonomy and improve consistency across batches.

Contributed to a large-scale, multimodal AI training initiative focused on improving the accuracy of natural language understanding, image recognition, and speech emotion detection systems. For text data, labeled and categorized over 15,000 samples from chat transcripts and social media posts for sentiment analysis, intent detection, and named entity recognition (NER). For image datasets, annotated thousands of objects using bounding boxes and segmentation tools to support object detection in urban and retail environments. Additionally, labeled audio clips for emotion and speaker recognition tasks in multilingual datasets (English and Spanish). Ensured data quality through double-review validation, inter-annotator agreement (IAA) checks exceeding 95%, and adherence to project-specific ontology and annotation guidelines. Collaborated with QA teams to refine taxonomy and improve consistency across batches.

2023 - 2025

Education

Work History