predict movie's genres based on overview
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Updated
Jul 15, 2024 - Python
predict movie's genres based on overview
Building this project to generate MCQ Questions from any type of text and generate answers and distractors for it.
This repository contains the code, models and corpus of the project "Generative Adversarial Networks for Text-to-Image Synthesis & Generation: A Comparative Analysis of Natural Language Processing models for the Spanish language".
This project analyzes tweets about electronic products using ReBERTa, Kafka, Logstash, Elasticsearch, and Kibana for sentiment analysis and data visualization.
This project uses the stsb-roberta-large sentence transformer model (deprecated) to check whether a set of given phrases match a certain phrase in meaning.
Resources for the paper: Monolingual Pre-trained Language Models for Tigrinya
Tutorial on training a RoBERTa Transformers model from scratch
BIRBAL.AI is a dynamic Gen AI-infused Interactive & Analytical Dashboard. Leveraging Meta’s Llama2, and RoBERTa alongside PygWalker for NLP-based Sentiment Analysis, Its AI Insights Engine pioneers Multi-Lingual & Voice interactive analytics, utilizing Bhashini APIs and Meta’s Llama2 across all 22 official languages of India
A project demonstrating the use of Large Language Models (LLMs) for text classification using the RoBERTa model.
A Streamlit chatbot app, providing interactive Q&A on Streamlit updates and more, with easy setup for local and EC2 deployment.
A web application built using the Django framework that allows users to analyze the sentiment of text inputs, providing insights into the emotional tone and polarity of the content.
🙂🙃 Being happy :) being sad :( with this tool, you become sentiment GIGA chad!
Generating code with Ludwig AI/ML (PyTorch, Tensorflow)
Studies show that people are more depressed than ever after the pandemic, but is the way we are measuring depression even accurate?
Best project ever
This work focuses on the development of machine learning models, in particular neural networks and SVM, where they can detect toxicity in comments. The topics we will be dealing with: a) Cost-sensitive learning, b) Class imbalance
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