Emotion Classification with SpaCy v3 & Comet
Blog post from Comet
SpaCy, a widely-used open-source natural language processing library in Python, is integrated with Comet ML, an experiment monitoring tool, to enhance the process of training a multi-label text classifier using the Huggingface dair-ai/emotion dataset. This dataset, consisting of 20,000 samples of Twitter messages categorized into six basic emotions—anger, fear, joy, love, sadness, and surprise—is divided into training, validation, and test sets. The blog post outlines the steps to prepare the dataset, convert it into a SpaCy-compatible format, and set up a configuration file for training with SpaCy v3. Integration with Comet ML allows for real-time tracking of training progress and results, ultimately producing two versions of the model: the best-performing and the last-trained. The model is evaluated using SpaCy's evaluation tools, demonstrating high performance across multiple emotion categories, and can be utilized to predict sentiments in new texts with confidence scores.
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