Dheker Amara

Key Skills

Software

Labelbox

LabelImg

Supervisely

Surge AI

Top Subject Matter

LLM Evaluation in French, English, Arabic, and Italian

E-commerce Product Categorization

Customer Service Chatbot Training

Top Data Types

Image

Text

Video

Top Task Types

Fine Tuning

Mapping

Object Detection

Prompt Response Writing SFT

Segmentation

Freelancer Overview

Experienced Data Annotator with a strong background in creating high-quality training datasets for AI and machine learning applications. Proficient in a range of labeling techniques, including bounding boxes, semantic segmentation, named entity recognition (NER), and text classification, across multiple data types such as image, text, and video. Skilled in using advanced annotation tools like LabelImg, Supervisely, Labelbox, and VGG Image Annotator (VIA) to deliver precise and consistent labeled data. My multilingual capabilities in English, French, Arabic, and Italian enable me to effectively contribute to diverse LLM evaluation projects. I have successfully managed large-scale data annotation projects in various industries, including self-driving car imagery, satellite image classification, and e-commerce product categorization. My meticulous approach to data quality and accuracy, combined with a deep understanding of AI model requirements, ensures that the datasets I produce significantly enhance model performance and reliability. My ability to collaborate closely with cross-functional teams and adapt quickly to new technologies makes me a valuable asset in any AI training data initiative.

Labeling Experience

Satellite Image Classification for Environmental Monitoring

SuperviselyImagePolygonClassification

Involved in a satellite image annotation project aimed at classifying various land cover types such as forests, water bodies, urban areas, and agricultural fields. Used polygon annotations to create detailed classifications, supporting environmental monitoring and land use analysis.

Involved in a satellite image annotation project aimed at classifying various land cover types such as forests, water bodies, urban areas, and agricultural fields. Used polygon annotations to create detailed classifications, supporting environmental monitoring and land use analysis.

2022 - 2022

LLM Evaluation Across Multiple Languages

LabelboxTextEntity Ner ClassificationText Summarization

Conducted extensive text annotation for large language models (LLMs) in English, French, Arabic, and Italian. Tasks included identifying entities, classifying text sentiment, and organizing text by category to improve language model understanding and accuracy. The project aimed to enhance the multilingual capabilities of AI models for various applications.

Conducted extensive text annotation for large language models (LLMs) in English, French, Arabic, and Italian. Tasks included identifying entities, classifying text sentiment, and organizing text by category to improve language model understanding and accuracy. The project aimed to enhance the multilingual capabilities of AI models for various applications.

2022 - 2022

Self-Driving Car Imagery Annotation

SuperviselyVideoBounding BoxSegmentation

Led a large-scale annotation project focused on creating training datasets for self-driving cars. The project involved annotating thousands of images and videos to identify objects such as vehicles, pedestrians, traffic signs, and lane markings. Ensured high accuracy and consistency across all labeled data to enhance the performance of computer vision models used in autonomous vehicles.

Led a large-scale annotation project focused on creating training datasets for self-driving cars. The project involved annotating thousands of images and videos to identify objects such as vehicles, pedestrians, traffic signs, and lane markings. Ensured high accuracy and consistency across all labeled data to enhance the performance of computer vision models used in autonomous vehicles.

2021 - 2022

Education

Work History