Key Skills

Software

Appen

Top Subject Matter

AI data labeling and annotation for multilingual text-based models in English, Arabic, and Malayalam

LLM evaluation and dataset curation for natural language understanding and generation tasks

Annotation for sentiment analysis and classification in multilingual content for AI systems.

Top Data Types

Document

Image

Text

Top Task Types

Classification

Evaluation Rating

Fine Tuning

Object Detection

Text Generation

Freelancer Overview

As an entry-level professional with less than a year of experience in data labeling and annotation, I have developed a strong understanding of preparing high-quality data for machine learning and AI model training. I have hands-on experience working with various types of data, including text, images, and video, ensuring that each dataset is accurately labeled, categorized, and formatted to meet the needs of AI systems. My work involves using different annotation tools and adhering to specific guidelines, ensuring consistency and precision in my outputs. During my short tenure, I have worked on several data labeling projects that required attention to detail and quick adaptation to new processes and tools. I am also continuously refining my skills in quality control, data accuracy, and understanding the nuances of AI training data requirements. My ability to learn quickly, communicate effectively, and meet deadlines has contributed to the success of the projects I have worked on

Labeling Experience

Multilingual Text Annotation and Classification for NLP Models

AppenTextClassification

Worked on a large-scale multilingual dataset to classify and annotate text for training language models. Tasks included identifying and tagging entities, categorizing sentiments, and ensuring linguistic accuracy in English, Arabic, and Urdu texts. Delivered high-quality annotations that enhanced the model's understanding of sentiment, grammar, and context, contributing to a 15% improvement in language generation performance. Utilized industry-leading tools like Appen and Label Studio to streamline annotation workflows.

Worked on a large-scale multilingual dataset to classify and annotate text for training language models. Tasks included identifying and tagging entities, categorizing sentiments, and ensuring linguistic accuracy in English, Arabic, and Urdu texts. Delivered high-quality annotations that enhanced the model's understanding of sentiment, grammar, and context, contributing to a 15% improvement in language generation performance. Utilized industry-leading tools like Appen and Label Studio to streamline annotation workflows.

2024

Education

Work History