Ashley Greavu

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Classification

Emotion Recognition

Evaluation Rating

Freelancer Overview

I have hands-on experience supporting AI and machine learning systems through high-quality data labeling and training data preparation. My work includes annotating text, image, audio, and video data with a strong focus on accuracy, consistency, and adherence to detailed guidelines. I’m experienced with tasks such as classification, bounding boxes, segmentation, sentiment analysis, entity recognition, and content moderation, and I consistently maintain quality standards through careful review and validation. I’m comfortable working with large datasets, iterative feedback, and evolving labeling schemas. What sets me apart is my strong attention to detail, analytical thinking, and ability to quickly understand complex instructions and edge cases. I’m efficient, reliable, and able to work independently while meeting tight deadlines. I also bring a solid understanding of how labeled data impacts model performance, which helps me make thoughtful annotation decisions rather than mechanical ones. Combined with clear communication skills and adaptability across tools and platforms, I contribute training data that is accurate, scalable, and genuinely useful for improving AI systems.

Labeling Experience

LLM Training and Content understanding

AppenTextClassificationEmotion Recognition

Worked on large-scale NLP data labeling projects to support the training and evaluation of large language models. Responsibilities included annotating text for named entity recognition (persons, locations, organizations, products), intent and topic classification, sentiment and emotion recognition, and response quality evaluation. The project involved labeling tens of thousands of text samples, including user queries, chatbot conversations, and generated responses. Strict annotation guidelines were followed, with special attention to edge cases, ambiguity resolution, and contextual accuracy. Quality measures included peer reviews, gold-standard tests, inter-annotator agreement checks, and continuous feedback loops to maintain high accuracy and consistency.

Worked on large-scale NLP data labeling projects to support the training and evaluation of large language models. Responsibilities included annotating text for named entity recognition (persons, locations, organizations, products), intent and topic classification, sentiment and emotion recognition, and response quality evaluation. The project involved labeling tens of thousands of text samples, including user queries, chatbot conversations, and generated responses. Strict annotation guidelines were followed, with special attention to edge cases, ambiguity resolution, and contextual accuracy. Quality measures included peer reviews, gold-standard tests, inter-annotator agreement checks, and continuous feedback loops to maintain high accuracy and consistency.

2023 - 2023

Education

Work History