Praise S.

Key Skills

Software

No software listed

Top Subject Matter

NLP sentiment analysis and text classification

Computer vision object detection (logo localization)

Legal Services & Contract Review

Top Data Types

Text

Video

Image

Document

Top Task Types

Emotion Recognition

Bounding Box

Freelancer Overview

AI Data Contributor | HiveMicro (Remote) - Text annotation for NLP. Professional background includes roles such as AI Data Contributor. Core strengths include HiveMicro. Education includes Bachelor of Engineering, Federal University Oye-Ekiti (2025). AI-training focus includes data types such as Text, Video, and Image and labeling workflows including Emotion Recognition and Bounding Box.

Labeling Experience

AI Data Contributor | HiveMicro (Remote) - Image bounding box labeling

ImageBounding Box

Completed image annotation to generate labeled training data for computer vision models. Focused on accurate localization using bounding-box style labels, including logo bounding boxes. Maintained high accuracy scores across varied image datasets used in ML pipelines. • Labeled images for object detection and localization. • Generated logo bounding box annotations. • Preserved label quality across diverse examples. • Supported model training with reliable ground truth.

2025 - Present

AI Data Contributor | HiveMicro (Remote) - Video/Image annotation

VideoBounding Box

Executed video and image annotation tasks to support computer vision training for object detection. Produced structured bounding-box style labels (including logo bounding box localization) required for accurate model learning. Tracked annotation quality through adherence to dataset instructions and evaluation criteria. • Created logo bounding box annotations for computer vision datasets. • Performed video annotation work for object detection training. • Managed annotation consistency across dataset variants. • Ensured labeling accuracy to preserve ground-truth integrity.

2025 - Present

AI Data Contributor | HiveMicro (Remote) - Text annotation for NLP

TextEmotion Recognition

Performed high-precision text annotation for NLP models to improve sentiment analysis and text classification quality. Focused on producing consistent, high-accuracy labels suitable for downstream machine learning pipelines. Ensured annotations matched the intended taxonomy and ground-truth standards for evaluation. • Labeled text examples for sentiment and classification tasks. • Maintained label consistency across diverse datasets. • Supported “ground truth” data quality for model training. • Calibrated annotation outputs for improved reliability.

2025 - Present

Education

Work History