Domain-Specific Data Annotation Pipeline
Label StudioTextClassification
I built an end-to-end data annotation pipeline to curate and label over 3,000 text samples for AI model fine-tuning. The work involved designing annotation guidelines, conducting quality control, and organizing data for supervised learning. This process achieved high inter-annotator agreement and ensured robust training data for NLP models.
• Curated and annotated text samples for categories such as intent classification, named entity recognition, and sentiment analysis
• Developed annotation guidelines and detailed quality control checklists
• Achieved inter-annotator agreement scores above 90%
• Utilized Label Studio and Python for data workflow management and annotation
I built an end-to-end data annotation pipeline to curate and label over 3,000 text samples for AI model fine-tuning. The work involved designing annotation guidelines, conducting quality control, and organizing data for supervised learning. This process achieved high inter-annotator agreement and ensured robust training data for NLP models. • Curated and annotated text samples for categories such as intent classification, named entity recognition, and sentiment analysis • Developed annotation guidelines and detailed quality control checklists • Achieved inter-annotator agreement scores above 90% • Utilized Label Studio and Python for data workflow management and annotation
2024 - 2024