This project generates news headlines using a Long Short-Term Memory (LSTM) neural network.

Overview

News Headlines Generator

bunnysaini/Generate-Headlines

Goal

This project aims to generate news headlines using a Long Short-Term Memory (LSTM) neural network working upon data collected (articles and comments) from the New York Times in 2020.

Dataset

The New York Times is one of the most popular online news platforms in the world. What sets the Times apart from other publications is the ability to engage and connect with its readers.This dataset contains all comments and articles from January 1, 2020 - December 31, 2020. The articles .csv file contains 16K+ articles with 11 features, and the comments .csv file contains nearly 5M comments with 23 features. https://www.kaggle.com/benjaminawd/new-york-times-articles-comments-2020

Libraries Used

  • Keras Library
  • Tensorflow

Result

image

Owner
Bunny Saini
Bunny Saini
A flexible ML framework built to simplify medical image reconstruction and analysis experimentation.

meddlr Getting Started Meddlr is a config-driven ML framework built to simplify medical image reconstruction and analysis problems. Installation To av

Arjun Desai 36 Dec 16, 2022
Official Pytorch implementation for 2021 ICCV paper "Learning Motion Priors for 4D Human Body Capture in 3D Scenes" and trained models / data

Learning Motion Priors for 4D Human Body Capture in 3D Scenes (LEMO) Official Pytorch implementation for 2021 ICCV (oral) paper "Learning Motion Prior

165 Dec 19, 2022
Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

OD-Rec Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation' Paper, saved teacher models and Andro

Xin Xia 11 Nov 22, 2022
This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.

Polygon-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes. Section I. Description The codes a

xinzelee 226 Jan 05, 2023
Pytorch GUI(demo) for iVOS(interactive VOS) and GIS (Guided iVOS)

GUI for iVOS(interactive VOS) and GIS (Guided iVOS) GUI Implementation of CVPR2021 paper "Guided Interactive Video Object Segmentation Using Reliabili

Yuk Heo 13 Dec 09, 2022
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis Figure: Shape-Accurate 3D-Aware Image Synthesis. A Shading-Guid

Xingang Pan 115 Dec 18, 2022
Reproducing-BowNet: Learning Representations by Predicting Bags of Visual Words

Reproducing-BowNet Our reproducibility effort based on the 2020 ML Reproducibility Challenge. We are reproducing the results of this CVPR 2020 paper:

6 Mar 16, 2022
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or

Katsuya Hyodo 10 Aug 30, 2022
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible

pyrelational is a python active learning library developed by Relation Therapeutics for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximat

Relation Therapeutics 95 Dec 27, 2022
MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network

MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network This repository is the official implementation of MatchGAN: A S

Justin Sun 12 Dec 27, 2022
Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL"

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL" This is the official codebase for Pessimism Meets I

3 Sep 19, 2022
Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

Segmenter: Transformer for Semantic Segmentation Segmenter: Transformer for Semantic Segmentation by Robin Strudel*, Ricardo Garcia*, Ivan Laptev and

594 Jan 06, 2023
A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

Use this instead: https://github.com/facebookresearch/maskrcnn-benchmark A Pytorch Implementation of Detectron Example output of e2e_mask_rcnn-R-101-F

Roy 2.8k Dec 29, 2022
Anonymous implementation of KSL

k-Step Latent (KSL) Implementation of k-Step Latent (KSL) in PyTorch. Representation Learning for Data-Efficient Reinforcement Learning [Paper] Code i

1 Nov 10, 2021
Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Christopher T. Chubb 35 Dec 21, 2022
Fully Convolutional Refined Auto Encoding Generative Adversarial Networks for 3D Multi Object Scenes

Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes This repository contains the source code for Full

Yu Nishimura 106 Nov 21, 2022
Code for the paper "Asymptotics of ℓ2 Regularized Network Embeddings"

README Code for the paper Asymptotics of L2 Regularized Network Embeddings. Requirements Requires Stellargraph 1.2.1, Tensorflow 2.6.0, scikit-learm 0

Andrew Davison 0 Jan 06, 2022
Learning to Self-Train for Semi-Supervised Few-Shot

Learning to Self-Train for Semi-Supervised Few-Shot Classification This repository contains the TensorFlow implementation for NeurIPS 2019 Paper "Lear

86 Dec 29, 2022
Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.

Lens by Credo AI - Responsible AI Assessment Framework Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data a

Credo AI 27 Dec 14, 2022