aims to develop a movie recommendation system utilizing Natural Language Processing.

This project build a classification model for topics of news. With the target is automatically recognize suitable topic (class) to a random article. There are two architectures implemented which are LSTM and Hybrid models

Implement the word embedding for exploring the correlation among words - Design a sequence model for generating text

Data preprocessing and training on Drug Review Dataset using Hugging Face library

A lightweight OCR model for Japanese text, especially in Manga

The voice-controlled robot system features four simple commands: forward, backward, left, and right.

This project provides a comprehensive analysis of tweet sentiment during the COVID-19 pandemic using Natural Language Processing (NLP) techniques. The dataset is classified into five sentiment classes, and thorough data exploration and preprocessing techniques are applied to handle imbalances. ML & DL models utilised.

Build an end to end pipeline for Named Entity Recognition (NER) by a pretrained Huggingface transformer, BERT and deploy to google cloud platform using Docker, CI/CD tool: CircleCI.

A system that summerizes long text document and generates translation in multiple language with the help of NLP models

Projet: Classification de tweets basée sur le transfert learning

SetFit Model 3: Annotation and Fine-Tuning Deep-Learning Model for Stance Detection

BERT Model 2: Annotation and Fine-Tuning Deep-Learning Model for Passage Boundary Detection

BERT Model 1: Annotating and Fine-Tuning Deep-Learning Model for Binary Text Classification

Bidirectional LSTM for performing a NLP task

DistilBERT for performing a NLP task

Transformer-encoder for performing a NLP task

Chatbot with Intent Pattern Recognition & Speech Recognition [WIP]

A multi-label text content classifier that can classify 103 different themes concurrently, given any Creative, poetry, Literary, Descriptive, synopsis, quotes, songs or Dialogues text content

Leverages extensive power of multiple Machine Learning algorithms & LLM to provide in-depth answers to medical queries and predicts condition/diseases based on patient symptoms

🛠️ BuggyBuddy Model Builder [Microservice] is a component of the BuggyBuddy project, serving as a Model Builder (Create, Train, Evaluate, Save). This Model Builder is designed to automatically train new models when new data is available, ensuring that the model remains relevant.