Reem Nobani

Key Skills

Software

Clickworker

HiveMind

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Emotion Recognition

Entity Ner Classification

Object Detection

Question Answering

Text Summarization

Freelancer Overview

"As an expert in AI and training data labeling, I possess strong experience in key areas including Emotion Recognition, Text Summarization, and Object Detection. I have acquired advanced skills in ensuring the accuracy and consistency of data crucial for developing high-performing AI models within these specialized domains. Through my work, I have developed a superior ability to understand the nuances in data and apply labeling standards efficiently and effectively." "My specialization in Emotion Recognition enables me to accurately classify emotional expressions in text and images, which is critical for applications such as sentiment analysis and customer service. In the field of Text Summarization, I have the ability to extract essential information and create concise, coherent summaries that preserve the original meaning. Furthermore, I have extensive experience in the precise labeling of objects in computer vision data, which is essential for applications like autonomous driving and security surveillance. These in-depth skills make me a valuable contributor to any project that relies on high-quality training data."

Labeling Experience

Labeling and Classification Project

ClickworkerImageEntity Ner ClassificationRelationship

Labeling Giving names or tags: Think of it like putting sticky notes on each item to say what it is. For example, in an image of a street, you might label a car as "car," a pedestrian as "person," and a traffic light as "traffic light." Adding attributes: You might go beyond just the name and add more details. For the "car" label, you could add attributes like "color: red," "make: Toyota," or "direction: left." For text, you might label the sentiment as "positive," "negative," or "neutral." Drawing boundaries: In computer vision, labeling often involves drawing boxes or outlines around objects of interest in an image or video. This tells the AI exactly where the "car" or "person" is located. 2. Classification Putting things into categories: This is about grouping similar items together based on their labels or attributes.

Labeling Giving names or tags: Think of it like putting sticky notes on each item to say what it is. For example, in an image of a street, you might label a car as "car," a pedestrian as "person," and a traffic light as "traffic light." Adding attributes: You might go beyond just the name and add more details. For the "car" label, you could add attributes like "color: red," "make: Toyota," or "direction: left." For text, you might label the sentiment as "positive," "negative," or "neutral." Drawing boundaries: In computer vision, labeling often involves drawing boxes or outlines around objects of interest in an image or video. This tells the AI exactly where the "car" or "person" is located. 2. Classification Putting things into categories: This is about grouping similar items together based on their labels or attributes.

2024

Education

Work History