There are 4 repositories under imdb-dataset topic.
Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0
Comparatively fine-tuning pretrained BERT models on downstream, text classification tasks with different architectural configurations in PyTorch.
This repository contains a DistilBERT model fine-tuned using the Hugging Face Transformers library on the IMDb movie review dataset. The model is trained for sentiment analysis, enabling the determination of sentiment polarity (positive or negative) within text reviews.
A Vagrant box that automatically loads the IMDB dataset into Postgres
š¬ An attempt at the most complete IMDb API
This repository contains analysis of IMDB data from multiple sources and analysis of movies/cast/box office revenues, movie brands and franchises.
Visualize the IMDB rating of every episode for any TV series.
Pytorch implementation of the paper Convolutional Neural Networks for Sentence Classification
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%.
Detect actor / actress faces in an image and list their work (movies / series)
:movie_camera: R data package to explore Pixar films, the people, and reception data
Repository of state of the art text/documentation classification algorithms in Pytorch.
Topics related to Deep Learning
Text Classification using Mamba Model
A machine learning model to recommend movies & tv series
Sentiment analysis of IMDB dataset.
Fetch movie data from IMDB and output in JSON format.
Builds a Microsoft SQL Server 2016+ relational database from IMDb official data files, to support personal querying.
Transfer Learning model using RoBERTa on IMDb dataset deployed on React and Flask ( Regional Winner in Facebook Developer Community Challenge 2020 )
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.
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)
Scrape Data From IMDB Movie DataBase
SQL queries performed on IMDb database to provide recommendations to RSVP Movies based on insights.
PyTorch implementation of the Gated Attention Network for text classification. Comparison of the model with BiLSTM and soft attention models using IMDb and TREC dataset.
This project aims at determinig the genre of the movie using its posters for image classifications CNNs are the most effective types of neural network in this project we try to create a CNN which would predict the genres of these movies.
Python package to both parse datsets provided by IMDb and scrape information from imdb.com
Detect spoilers in IMDb movie reviews with deep neural network
RSVP Movies is an Indian film production company which has produced many super-hit movies. They have usually released movies for the Indian audience but for their next project, they are planning to release a movie for the global audience in 2022.
Research oriented, developing word embeddings for binary text-polarity classifier based on movie reviews using BoW, TF-IDF, n-Gram, Word2Vec, Doc2Vec, PV-DM, PV-DBOW and other NLP techniques.
Exploratory analysis on the IMDB movie database
Some of my projects and notebooks related to Machine Learningš in one place and not scattered all over GitHub. Check out the README.md in the respective folders first.
Bunch of examples of a "Simple but tough to beat baseline for sentence embeddings" in classification tasks
This Movie Recommendation System is a Python-based application that leverages machine learning and natural language processing techniques to provide movie recommendations based on user preferences and movie characteristics.