The goal of this repository is to build an end-to-end MLOps pipeline based on the MLOps course from Made with ML, but this project have some differences on design, tools and frameworks used, with the objective to practice and give a different angle and implementation to the original course.
This project uses a BERT model for emotion classification and is based on the GoEmotions dataset.
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Taken from https://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html
In “GoEmotions: A Dataset of Fine-Grained Emotions”, we describe GoEmotions, a human-annotated dataset of 58k Reddit comments extracted from popular English-language subreddits and labeled with 27 emotion categories. As the largest fully annotated English language fine-grained emotion dataset to date, we designed the GoEmotions taxonomy with both psychology and data applicability in mind. In contrast to the basic six emotions, which include only one positive emotion (joy), our taxonomy includes 12 positive, 11 negative, 4 ambiguous emotion categories and 1 “neutral”, making it widely suitable for conversation understanding tasks that require a subtle differentiation between emotion expressions.
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