Update README.md

README.md

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 short_description: Emotion Classification
 ---
 
+Emotion Classifier
+
+1. Overview
+
+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:
+
+* GPT-4
+
+* Llama-3.2
+
+* T5 Base
+
+The models are deployed on Hugging Face Spaces using Gradio, offering a real-time, interactive web-based interface for emotion classification.
+
+2. Live Demo
+
+Interact with the emotion classifier models in real-time by entering a text sample and selecting a model for classification.
+
+Link: https://huggingface.co/spaces/HaryaniAnjali/Emotion_Classification 
+
+3. Models Used
+
+     * 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.
+
+
+4. Dataset
+
+The dataset used for training consists of text samples labeled with seven emotions. 
+
+5. Implementation Details
+
+   * Data Preprocessing: Text cleaning (alphnumeric code, punctuation removal).
+
+
+   * 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.
+
+   * Model Deployment: Fine-tuned models were uploaded to Hugging Face Model Hub.
+
+   * Performance Analysis The models were evaluated using accuracy, precision, recall, and F1-score. Here are the results:
+  
+     
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/67aa7cb6c6abc0f7e891d922/F15Sp2zmgNfsxkAczMmQb.png)
+
+   * 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.
+
+6. How to Use
+
+* 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.
+
 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).