Emotion Detection Model Overview This model is a fine-tuned DistilBERT classifier designed for detecting emotions in text inputs. It identifies four primary emotions: happy, sad, angry, and neutral. Trained on a diverse dataset of social media posts and reviews, it achieves high accuracy in real-world applications. Model Architecture The model uses DistilBERT as the base architecture, a distilled version of BERT with 6 layers, 768 hidden units, and 12 attention heads. It includes a sequence classification head on top for emotion labeling. Intended Use This model is intended for applications such as sentiment analysis in customer feedback, mental health monitoring in chatbots, or content moderation on social platforms. It processes English text inputs up to 512 tokens. Limitations The model may struggle with sarcastic or ambiguous text, non-English languages, or domain-specific jargon. It was trained on general data and might require fine-tuning for specialized use cases. Bias in training data could lead to skewed predictions for certain demographics.
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