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

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

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

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

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

Efficient Text Summarization: Generating Concise Highlights

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.

Classify movie genre from its summary.

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

The region containing the face is blurred in Realtime

Classify 160 different tags from scifi and fantasy questions.

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

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

Deep learning multi-label classification for patent CPC code prediction.