Munzila Khatun

Key Skills

Software

CVAT

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Top Label Types

Bounding Box

Entity Ner Classification

Classification

Object Detection

Evaluation Rating

Question Answering

Emotion Recognition

Text Summarization

Freelancer Overview

I am a detail-oriented Computer Science professional with a PhD and hands-on experience in data annotation for both text and images. My background includes working with annotation tools such as CVAT and Labelbox, where I have performed tasks like text classification, sentiment labeling, intent tagging, and drawing bounding boxes on images. I am comfortable following complex labeling specifications, incorporating feedback, and adapting quickly to new tools and workflows. My expertise in AI, machine learning, and data analysis enables me to understand the importance of high-quality training data, and I am committed to delivering accurate and reliable results in data labeling projects.

Labeling Experience

Text & Image Data Annotation for AI/ML Research Models

CVATImageBounding BoxEntity Ner Classification

Performed structured text and image data annotation to support AI/ML research and model development. Text annotation tasks included classification, sentiment labeling, intent tagging, and named entity recognition (NER) following predefined annotation guidelines. Image annotation involved drawing bounding boxes on general, non-specialized images using CVAT, with a focus on object identification and labeling accuracy. Worked on tens of images and multiple text datasets, ensuring consistency, correctness, and adherence to quality standards. Reviewed edge cases, flagged ambiguous data points, and incorporated reviewer feedback to improve annotation quality. Maintained high attention to detail while following project specifications and version-controlled workflows.

Performed structured text and image data annotation to support AI/ML research and model development. Text annotation tasks included classification, sentiment labeling, intent tagging, and named entity recognition (NER) following predefined annotation guidelines. Image annotation involved drawing bounding boxes on general, non-specialized images using CVAT, with a focus on object identification and labeling accuracy. Worked on tens of images and multiple text datasets, ensuring consistency, correctness, and adherence to quality standards. Reviewed edge cases, flagged ambiguous data points, and incorporated reviewer feedback to improve annotation quality. Maintained high attention to detail while following project specifications and version-controlled workflows.

2023 - 2024

Text Annotation & Content Classification for NLP Model Training

LabelboxTextEntity Ner ClassificationQuestion Answering

Contributed to NLP-focused data labeling projects aimed at improving the performance of language models and text analytics systems. Performed detailed text annotation tasks including content classification, sentiment and emotion labeling, intent detection, and named entity recognition (NER). Annotated multi-domain textual data such as short passages, user queries, and structured responses following strict labeling guidelines. Ensured annotation consistency and high accuracy by cross-checking labels, handling edge cases, and applying reviewer feedback. Supported downstream tasks such as text summarization and question-answering dataset preparation by maintaining clarity, relevance, and contextual correctness in annotations.

Contributed to NLP-focused data labeling projects aimed at improving the performance of language models and text analytics systems. Performed detailed text annotation tasks including content classification, sentiment and emotion labeling, intent detection, and named entity recognition (NER). Annotated multi-domain textual data such as short passages, user queries, and structured responses following strict labeling guidelines. Ensured annotation consistency and high accuracy by cross-checking labels, handling edge cases, and applying reviewer feedback. Supported downstream tasks such as text summarization and question-answering dataset preparation by maintaining clarity, relevance, and contextual correctness in annotations.

2024

Education

Work History