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README.md

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-license: gpl-3.0
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+---
+license: gpl-3.0
+---
+
+# Hi, I’m Seniru Epasinghe 👋
+
+I’m an AI undergraduate and an AI enthusiast, working on machine learning projects and open-source contributions.  
+I enjoy exploring AI pipelines, natural language processing, and building tools that make development easier.
+
+## 🌐 Connect with me
+
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-seniruk-orange?logo=huggingface&logoColor=white)](https://huggingface.co/seniruk)   
+[![Medium](https://img.shields.io/badge/Medium-seniruk_epasinghe-black?logo=medium&logoColor=white)](https://medium.com/@senirukepasinghe)   
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-seniru_epasinghe-blue?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/seniru-epasinghe-b34b86232/)   
+[![GitHub](https://img.shields.io/badge/GitHub-seth2k2-181717?logo=github&logoColor=white)](https://github.com/seth2k2)
+---
+
+# Multimodal Emotion Recognition Dataset (Processed from MELD)
+
+This dataset is a **preprocessed and balanced version** of the [MELD Dataset](https://www.kaggle.com/datasets/zaber666/meld-dataset), designed for **multimodal emotion recognition research**.  
+It combines **text, audio, and video modalities**, each represented by a set of **emotion probability distributions** predicted by pretrained or custom-trained models.
+
+---
+
+## Overview
+
+| Feature | Description |
+|----------|--------------|
+| **Total Samples** | 4,000 utterances |
+| **Modalities** | Text, Audio, Video |
+| **Balanced Emotions** | Each emotion class is approximately balanced |
+| **Cleaned Samples** | Videos with unclear or no facial detection removed |
+| **Emotion Labels** | `['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']` |
+
+Each row in the dataset corresponds to a single utterance, along with emotion label, file name, and predicted emotion probabilities per modality.
+
+---
+
+## Example Entry
+
+| Utterance | Emotion | File_Name | MultiModel Predictions |
+|------------|----------|------------|----------------|
+| You are going to a clinic! | disgust | dia127_utt3.mp4 | {"video": [0.7739, 0.0, 0.0, 0.0783, 0.1217, 0.0174, 0.0087], "audio": [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], "text": [0.0005, 0.0, 0.0, 0.0007, 0.998, 0.0004, 0.0004]} |
+
+### Column Description:
+- **Utterance** — spoken text in the conversation.  
+- **Emotion** — gold-standard emotion label.  
+- **File_Name** — corresponding video file (utterance-level).  
+- **MultiModel Predictions** — JSON object containing model-predicted emotion probability vectors for each modality.
+
+---
+
+## Modality Emotion Extraction
+
+Each modality’s emotion vector was generated independently using specialized models:
+
+| Modality | Model / Method | Description |
+|-----------|----------------|--------------|
+| **Video** | [`python-fer`](https://github.com/justinshenk/fer) | Facial expression recognition using CNN-based FER library. |
+| **Audio** | [`Custom-trained CNN model`](https://medium.com/@senirukepasinghe/speech-emotion-recognition-with-cnn-8e3c2cbc8375) | Trained on Mel spectrogram features for emotion classification. |
+| **Text** | [`arpanghoshal/EmoRoBERTa`](https://huggingface.co/arpanghoshal/EmoRoBERTa) | Transformer-based text emotion model fine-tuned on GoEmotions dataset. |
+
+---
+
+## Format and Usage
+
+- File format: **CSV**
+- Recommended columns:
+  - `Utterance`
+  - `Emotion`
+  - `File_Name`
+  - `Final_Emotion` (JSON: `{ "video": [...], "audio": [...], "text": [...] }`)
+  
+This dataset is ideal for:
+- **Fusion model training**
+- **Fine-tuning multimodal emotion models**
+- **Benchmarking emotion fusion strategies**
+- **Ablation studies on modality importance**
+
+---
+
+## Citation
+
+References for the original MELD Dataset
+- S. Poria, D. Hazarika, N. Majumder, G. Naik, R. Mihalcea, E. Cambria. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation (2018).
+- Chen, S.Y., Hsu, C.C., Kuo, C.C. and Ku, L.W. EmotionLines: An Emotion Corpus of Multi-Party Conversations. arXiv preprint arXiv:1802.08379 (2018).
+
+---
+
+## License & Acknowledgments
+
+This dataset is a **derivative work** of MELD, used here for research and educational purposes.  
+All credit for the original dataset goes to the **MELD authors** and contributors.