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| title: Emotion Classification |
| emoji: 💬 |
| colorFrom: yellow |
| colorTo: purple |
| sdk: gradio |
| sdk_version: 5.0.1 |
| app_file: app.py |
| pinned: false |
| short_description: Emotion Classification |
| --- |
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| Emotion Classifier |
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| 1. Overview |
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| This project classifies emotions in textual data using advanced Large Language Models (LLMs). Three models have been fine-tuned on a structured emotion dataset to detect seven primary emotions: anger, disgust, fear, guilt, joy, sadness, and shame. The models used are: |
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| * GPT-4 |
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| * Llama-3.2 |
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| * T5 Base |
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| The models are deployed on Hugging Face Spaces using Gradio, offering a real-time, interactive web-based interface for emotion classification. |
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| 2. Live Demo |
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| Interact with the emotion classifier models in real-time by entering a text sample and selecting a model for classification. |
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| Link: https://huggingface.co/spaces/HaryaniAnjali/Emotion_Classification |
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| 3. Models Used |
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| * GPT-4: A state-of-the-art language model optimized for text understanding. |
| * Llama-3.2: A fine-tuned 7B parameter model for emotion classification. |
| * T5 Base: A smaller yet efficient model designed for prompt-based emotion detection. |
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| 4. Dataset |
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| The dataset is ISEAR Dataset used for this project consists of text samples labeled with seven emotions. |
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| 5. Implementation Details |
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| * Data Preprocessing: Text cleaning (alphnumeric code, punctuation removal). |
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| * Model Fine-Tuning: Fine-tuning was performed using prompt-based training strategies for all models (GPT-4, Llama-3.2, and T5 Base). |
| GPT-4, Llama-3.2, and T5 Base models were trained with specialized prompts to improve emotion classification. |
| The training approach included using structured prompts to guide the models in recognizing and classifying emotions in text. |
| This method helped the models focus on the task of emotion recognition from natural language inputs, improving their ability to understand context and nuances in emotions. |
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| * Model Deployment: Fine-tuned models were uploaded to Hugging Face Model Hub. |
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| * Performance Analysis The models were evaluated using accuracy, precision, recall, and F1-score. Here are the results: |
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| * Key Findings: GPT-4 demonstrated the best performance with the highest precision (0.8006) and accuracy (0.7399). Llama-3.2 showed good performance, but with slightly lower accuracy compared to GPT-4. T5 Base was effective but had slightly lower scores across the board. |
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| 6. How to Use |
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| * Enter a text input in the provided text box. |
| * Click the "Submit" button to classify the emotion. |
| * The predicted emotion will be displayed on the interface. |
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| An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index). |
