Hamzat Mutmahinat

Key Skills

Software

CVAT

Other

Top Subject Matter

Natural Language Processing (NLP)

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Medical Dicom

Text

Image

Document

Top Task Types

Segmentation

Classification

Freelancer Overview

Freelance AI Data Annotator. Core strengths include Other. AI-training focus includes data types such as Text and labeling workflows including Classification.

Labeling Experience

Nurse | medical Data Annonator

CVATMedical DicomSegmentationClassification

Leveraging my Nursing background and deep knowledge of human anatomy and physiology, and medical terminology to provide high-quality data annotation. i specialize in identifying anatomical structures and distinguishing between normal and abnormal clinical findings. My clinical training ensures a high level of attention of details and strict adherence to data privacy protocols.

Leveraging my Nursing background and deep knowledge of human anatomy and physiology, and medical terminology to provide high-quality data annotation. i specialize in identifying anatomical structures and distinguishing between normal and abnormal clinical findings. My clinical training ensures a high level of attention of details and strict adherence to data privacy protocols.

2023

Freelance AI Data Annotator

OtherTextClassification

Conducted text classification and sentiment analysis on extensive datasets to support language model development, particularly for regional dialects. Focused on improving natural language understanding and language diversity in AI applications. Applied consistent labeling guidelines to ensure accurate and reproducible dataset outcomes. • Labeled text for classification and sentiment analysis tasks. • Supported model accuracy in multilingual and regional context processing. • Applied strict data quality and standardization practices in text annotation. • Delivered annotated corpora for natural language AI training.

Conducted text classification and sentiment analysis on extensive datasets to support language model development, particularly for regional dialects. Focused on improving natural language understanding and language diversity in AI applications. Applied consistent labeling guidelines to ensure accurate and reproducible dataset outcomes. • Labeled text for classification and sentiment analysis tasks. • Supported model accuracy in multilingual and regional context processing. • Applied strict data quality and standardization practices in text annotation. • Delivered annotated corpora for natural language AI training.

Present

Education

Work History