Olofu Ekah

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Freelancer Overview

I am a detail-oriented Data Annotation Specialist with hands-on experience in labeling and categorizing datasets for AI and machine learning projects. My work spans both image and text annotation, including drawing bounding boxes for object detection in computer vision tasks and performing sentiment analysis and text classification for NLP datasets. I am proficient with annotation tools like LabelImg, CVAT, and Labelbox, and I consistently deliver high-quality, accurate training data by following strict guidelines. My background in data entry and organization using Microsoft Excel and Google Sheets further supports my ability to maintain data integrity and quality, ensuring reliable results for AI model development.

Labeling Experience

Mr Carter

LabelboxTextEntity Ner Classification

Contributed to a Named Entity Recognition (NER) dataset creation project designed to improve the performance of natural language processing models. The project involved annotating thousands of text samples including news articles, customer support messages, and web content. My main responsibilities included identifying and labeling key entities such as PERSON, ORGANIZATION, LOCATION, DATE, PRODUCT, and EVENT within sentences and paragraphs. I carefully followed detailed annotation guidelines to ensure consistency and accuracy across the dataset. The project required reviewing complex linguistic contexts to correctly classify entities, resolving ambiguous cases, and maintaining structured annotations. I also participated in quality assurance reviews, cross-checking annotations and correcting inconsistencies to maintain a quality score above 97%. Overall, the annotated dataset helped train and fine-tune NER models used in information extraction systems and LLM applications.

Contributed to a Named Entity Recognition (NER) dataset creation project designed to improve the performance of natural language processing models. The project involved annotating thousands of text samples including news articles, customer support messages, and web content. My main responsibilities included identifying and labeling key entities such as PERSON, ORGANIZATION, LOCATION, DATE, PRODUCT, and EVENT within sentences and paragraphs. I carefully followed detailed annotation guidelines to ensure consistency and accuracy across the dataset. The project required reviewing complex linguistic contexts to correctly classify entities, resolving ambiguous cases, and maintaining structured annotations. I also participated in quality assurance reviews, cross-checking annotations and correcting inconsistencies to maintain a quality score above 97%. Overall, the annotated dataset helped train and fine-tune NER models used in information extraction systems and LLM applications.

2024 - 2024

Education

Work History