Comparatively fine-tuning pretrained BERT models on downstream, text classification tasks with different architectural configurations in PyTorch.

Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0

A Vagrant box that automatically loads the IMDB dataset into Postgres

🎬 An attempt at the most complete IMDb API

Text Classification using Mamba Model

Visualize the IMDB rating of every episode for any TV series.

In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.

Pytorch implementation of the paper Convolutional Neural Networks for Sentence Classification

:movie_camera: R data package to explore Pixar films, the people, and reception data

Nano-BERT is a straightforward, lightweight and comprehensible custom implementation of BERT, inspired by the foundational "Attention is All You Need" paper. The primary objective of this project is to distill the essence of transformers by simplifying the complexities and unnecessary details.

Detect actor / actress faces in an image and list their work (movies / series)

A Django-based movie recommender that uses a trained ML model with a Jupyter notebook for customization. Features an interactive web UI with auto-suggestions and supports both local setup and cloud deployment.

Sentiment analysis of IMDB dataset.

Topics related to Deep Learning

IMDb verilerini toplayarak en popüler filmleri ve türleri analiz edip gösteren prototip bir web projesi.

A machine learning model to recommend movies & tv series

Fetch movie data from IMDB and output in JSON format.

This Python script extracts comprehensive movie data from IMDB, focusing on top-grossing movies from 1920 to 2025. The scraper collects detailed information including box office performance, cast & crew, awards, and other key metrics.

SQL queries performed on IMDb database to provide recommendations to RSVP Movies based on insights.

build a database from IMDb datasets

Repository of state of the art text/documentation classification algorithms in Pytorch.

Used Transformer's Encoder to classify movie reviews. From scratch

Builds a Microsoft SQL Server 2016+ relational database from IMDb official data files, to support personal querying.

IMDB Movie Reviews - Text preprocessing and classification. Includes BOW model, TF_IDF, VADER entiment analysis, Topic Modelling using Latent Dirichlet Allocation and Word Embeddings. (Python)

Transfer Learning model using RoBERTa on IMDb dataset deployed on React and Flask ( Regional Winner in Facebook Developer Community Challenge 2020 )

🔎 Aren't you fed up with the Netflix recommendations that keep looping through the same shows you've already watched 100 times? Come check this!

Detect spoilers in IMDb movie reviews with deep neural network

APIs for fetching basic movie information from IMDB.

Fine-tuning a RoBERTa model for sentiment analysis on the IMDB movie reviews dataset using the Adapter method and PyTorch Transformers

Scrape Data From IMDB Movie DataBase