The world's simplest facial recognition api for .NET on Windows, MacOS and Linux
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Updated
May 13, 2023 - C#
The world's simplest facial recognition api for .NET on Windows, MacOS and Linux
A Survey and Experiments on Annotated Corpora for Emotion Classification in Text
Pytorch Implementation of GoEmotions 😍😢😱
3DiVi Face SDK is a set of software components (code libraries) for building face recognition solutions
This repository contains the official release of the model "BanglaBERT" and associated downstream finetuning code and datasets introduced in the paper titled "BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla" accpeted in Findings of the Annual Conference of the North American Chap…
Indonesian twitter dataset for emotion classification task
An emotion classifier of text containing technical content from the SE domain
Korean version of GoEmotions Dataset 😍😢😱
Transfer learning for multi source EEG-emotion-classification
The repo contains an audio emotion detection model, facial emotion detection model, and a model that combines both these models to predict emotions from a video
Project Babble Module for VRCFaceTracking v5. An open-source VR mouth tracking solution
基于 RoBERTa-wwm-ext 模型的微博中文情绪识别
EmoEvent: A Multilingual Emotion Corpus based on different Events
Emotionally responsive Virtual Metahuman CV with Real-Time User Facial Emotion Detection (Unreal Engine 5).
🎭 다음(Daum) 뉴스 플랫폼에서 수집한 댓글 데이터에 대한 6가지 감정 분석
A mini-project on emotion classification using NLP for the course SC1015: Introduction to Data Science & Artificial Intelligence.
Original dissertation title: "Extract Emotional Tags from Movie Synopses". The identification, definition, and automatic prediction of a set of emotions in movies, based on their movie abstracts and various metadata.
Logistic regression, text emotion classifier web application (with Streamlit), from data preprocession to model productionizing and deployment on Streamlit share.
[RAVDESS] Speech Emotion Recognition with Convolutional Attention based Bi-GRU. (Best test accuracy of 87%)
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