Implementation of the paper 'Gyroscope-Aided Motion Deblurring with Deep Networks' https://arxiv.org/abs/1810.00986

Scraping paper data, preprocessed and trained using BERT variants, deployment and an integration to website

Classifying, summarzing and topic extracting from text transcribing from video or audio soruces.

This ML Model will help you to choose desired laptop based on your given information & requirements.

A sophisticated multilabel text classification model to seamlessly categorize diverse quote tags.

A multi-label text classification model which can classify the tasks based on the abstract of publication paper.

A multilabel text classifier model which can predict book genres based on the description provided.

Efficient Text Summarization: Generating Concise Highlights

A text classification model from data collection, model training, and deployment.

This project focuses on classifying video games into multiple genres using Natural Language Processing (NLP) techniques. Leveraging a pre-trained model from Hugging Face, the system predicts relevant genres based on game descriptions or related text data. The implementation demonstrates effective handling of multilabel classification challenges and

A multi-label text classifier that can classify 69 different programming related question category based on question description.

Classify movie genre from its summary.

The region containing the face is blurred in Realtime

Classify 160 different tags from scifi and fantasy questions.

Multilabel Dataset Category Classifer which can classify different types of dataset category based on description.