Bravin Ratemo

Key Skills

Software

Mindrift

Scale AI

Toloka

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Text

Top Task Types

Audio Recording

Question Answering

Text Summarization

Translation/Localization

Freelancer Overview

My background includes extensive experience in data annotation and quality assurance for AI training data. I have contributed to a variety of projects, including the classification of images for computer vision models, the annotation of text data for natural language processing, and the evaluation of AI model outputs for accuracy and relevance. I am highly proficient in adhering to complex annotation guidelines, ensuring data integrity, and providing detailed feedback to improve project efficiency and model performance.

Labeling Experience

Moderation

Data Annotation TechTextQuestion AnsweringText Generation

Natural Language Processing (NLP) Sentiment Analysis for Customer Feedback: Classified thousands of customer reviews and social media comments as positive, negative, or neutral. This project involved tagging key phrases and identifying the emotional tone to train a model for automated sentiment analysis. Named Entity Recognition (NER) in Legal Documents: Annotated legal contracts and case files to identify and label specific entities such as names of individuals, organizations, dates, and key legal terms. This work was crucial for developing a system to automatically extract and summarize information from complex legal texts. Chatbot Conversation Evaluation: Evaluated and scored conversational responses from a large language model (LLM) based on criteria like relevance, coherence, helpfulness, and tone. Provided corrected responses to improve the model's performance and safety. Computer Vision (Image/Video) Object Detection for Autonomous Vehicles

Natural Language Processing (NLP) Sentiment Analysis for Customer Feedback: Classified thousands of customer reviews and social media comments as positive, negative, or neutral. This project involved tagging key phrases and identifying the emotional tone to train a model for automated sentiment analysis. Named Entity Recognition (NER) in Legal Documents: Annotated legal contracts and case files to identify and label specific entities such as names of individuals, organizations, dates, and key legal terms. This work was crucial for developing a system to automatically extract and summarize information from complex legal texts. Chatbot Conversation Evaluation: Evaluated and scored conversational responses from a large language model (LLM) based on criteria like relevance, coherence, helpfulness, and tone. Provided corrected responses to improve the model's performance and safety. Computer Vision (Image/Video) Object Detection for Autonomous Vehicles

2022 - 2025

Education

Work History