This project explores building a transformer-based model that not only performs text classification tasks but also integrates emotional intelligence into its predictions. The goal is to develop a model capable of identifying and classifying emotions in text data, enabling it to respond empathetically to user inputs. By utilizing state-of-the-art transformer architectures such as BERT, the model is fine-tuned to detect a wide range of emotions from textual data, creating more emotionally aware AI systems.
Transformers (Hugging Face's BERT): Used for building the core model architecture, leveraging pre-trained weights for efficient and robust language understanding. Emotion Detection: The model is trained to detect and classify various emotions like happiness, sadness, anger, etc., based on user input. PyTorch: For building and training the model, utilizing PyTorch’s flexible deep learning framework. Datasets: A variety of emotion-labeled text datasets for training and evaluating the model.
Traditional text classification models often fail to recognize the emotional tone behind user inputs, limiting their ability to engage in human-like, empathetic conversations. By detecting emotions in the text, the model goes beyond basic sentiment analysis and can identify subtle emotional cues, providing more contextually appropriate responses for tasks like customer service, mental health support, and personalized recommendations.
The model’s performance is evaluated on emotion detection tasks, using metrics like accuracy, precision, recall, and F1 score to assess its ability to correctly classify emotions in text. The final trained model can be further integrated into any application requiring emotionally intelligent responses.
Model Architecture:
The model uses BERT for Sequence Classification:
Input: Sequences of tokens (words/subwords) are embedded using BERT's embedding layers, including word, position, and token type embeddings.
Transformer Layers: The model processes the sequence through 12 layers of BERT's transformer, which includes attention mechanisms and feed-forward networks.
Pooling: The output of the transformer layers is aggregated using a pooling layer, followed by a dense layer and activation function (Tanh) to create a fixed-size representation for the entire sequence.
Classifier: A final linear layer maps the hidden state to the output space, classifying the emotion in the text into one of several categories.
This description showcases your AI skills, focusing on model architecture, fine-tuning, and emotion detection rather than an end-to-end chatbot application. It emphasizes the application of transformers to a novel problem domain and highlights the use of Hugging Face and PyTorch to build a cutting-edge AI model.