SemEval2024-task 11: Bridging the Gap in Text-Based Emotion Detection
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Updated
Aug 6, 2024
SemEval2024-task 11: Bridging the Gap in Text-Based Emotion Detection
Emotion text classification using Llama3-8b with LoRA and FlashAttention. Based on LLaMA-Factory.
3DiVi Face SDK is a set of software components (code libraries) for building face recognition solutions
Project Babble Module for VRCFaceTracking v5. An open-source VR mouth tracking solution
Emotionally responsive Virtual Metahuman CV with Real-Time User Facial Emotion Detection (Unreal Engine 5).
TextPredict is a powerful Python package designed for various text analysis and prediction tasks using advanced NLP models. It simplifies the process of performing sentiment analysis, emotion detection, zero-shot classification, named entity recognition (NER), and more.
GiMeFive: Towards Interpretable Facial Emotion Classification ππ²ππ‘π€’π¨ (PyTorch Implementation)
A flexible text emotion classifier with support for multiple models, customizable preprocessing, visualization tools, fine-tuning capabilities, and more.
A Django-based web application designed to classify emotions from audio signals. This project includes a machine learning model implemented in a Jupyter Notebook for training and testing purposes. The web app allows users to upload audio files, which are then analyzed to determine the emotional content.
An emotion classifier of text containing technical content from the SE domain
Mobile Messanger Application with Automatic Facial Emotion Classification
Predict emotions (happiness, anger, sadness) from WhatsApp chat data using machine learning and deep learning models. Includes text normalization, vectorization (TF-IDF, BoW, Word2Vec, GloVe), and model evaluation.
Official code repository for paper "Multi-modal Speech Emotion Recognition using Multi-head Attention Fusion of Multi-feature Embeddings". Paper accepted to EAI INISCOM 2023
The detection of emotion is made by using the machine learning concept. You can use the trained dataset to detect the emotion of the human being. For detecting the different emotions, first, you need to train those different emotions, or you can use a dataset already available on the internet.
Real-time Emotion Recognition using Physiological signals in e-Learning Here one can find the development of realtime emotion recognition using various physiological signals
Unofficial implementation of the EmotionROI essay and extended applications
Show me how do I feel now.. ππ²π€’π¨ππ‘
The diploma and research project focuses on exploring the correlation between emotion classification and head pose orientation.
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