bigru

⚡ 基于PyTorch的多种模型文本分类框架

中文情感分类 | 基于三分类的文本情感分析

A ML project specifically build for predicting students' mental health

Medical Question Answering System.

A real-time fake news detection system that analyzes online news articles using machine learning. The project fetches live news, evaluates credibility based on trusted patterns, and shows how likely a news article is to be true, helping users make informed decisions.

M.Sc. mini project for NLP class (M908)

Source code and the details of the results in the paper "Named entity recognition in Turkish: A comparative study with detailed error analysis".

Simulation of Nanopore Sequencing Signals Based on BiGRU

👾 리그오브레전드 소환사 이름 기반 티어 예측 & 워드클라우드 분석

Full academic guidance for building your own speech recognition model from scratch.

This repository hosts a high-performance deep learning model designed to identify misogynistic content in Arabic social media text. Developed as an academic project, the model successfully overcomes significant linguistic challenges, such as dialectal variation and high Out-of-Vocabulary (OOV) rates.

Made a Image Caption Generator

Deep learning models and fine-tuned transformers for detecting mental illness from user content on Reddit. 🧠

Implementation of CNN and bi-GRU in parallel to solve text classification. In this particular example, I and my colleague, Ida Novindasari, use the AG news dataset from torchtext.datasets.

Our project aims at classifying the given news articles as fake or true based on the content using few-shot learning models with fewer data.

Predicted Delhi’s daily climate using Autoregressive Integrated Moving Average (ARIMA), Prophet, and Recurrent Neural Network (RNN) models for time series forecasting.

toxic comment classification using various model

Deep learning NLP project that classifies text into six emotions using an Advanced Bidirectional GRU (BiGRU). Includes text preprocessing, tokenization, model inference, FastAPI deployment, and an interactive web interface.

End-to-end Deep Learning application for 6-class emotion classification from text using pretrained MiniLM token embeddings and a BiGRU sequence classifier. The project covers text preprocessing, transformer-based representation learning, sequence modeling, model comparison, evaluation, confidence thresholding, and deployment through FastAPI