mousamohammed Goto Github PK
Type: User
Company: Shell
Location: Amsterdam, Netherland
Type: User
Company: Shell
Location: Amsterdam, Netherland
Text Summarization with Pretrained Encoders
My fork of BerSum, altered to summarize Arxiv Papers
Interactive deep learning book with code, math, and discussions. Available in multi-frameworks.
A RST Parser with a trained model
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Deep Learning algorithm for Image recognition challenge
In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.
Deep neural networks and convolutional neural networks to classify German traffic signs.
nla's workflow so dope u'd be jealousss
Code for ACL 2020 paper: "Extractive Summarization as Text Matching"
An NLP project in Summarization, Columbia University
Joint Extraction & Compression text Summarization
Text summarization is a very challenging problem that can only be truly realized by understanding meaning of the textual content. Recently, deep recurrent neural networks have been used in the sequence to sequence framework to achieve good results on summarization tasks. In this project we explore the success achieved by a stacked LSTM encoder – attention based decoder architecture on the English Gigaword dataset. We employ the usual best practices of sequence to sequence models with a complete end-to-end training, and have decent results to show on generating summaries of 10 words or less, for 2 line texts with a maximum of 30 words. We also explore on improvements in network training time, computation and refinement of summaries through various experiments.
The results of toy example summaries and DUC corpus evaluated with different python libraries based on ROUGE metric
summary of ML papers I've read
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Python text summarizer evaluation tool using ROUGE methods
SideNet: Neural Extractive Summarization with Side Information
Text classifier for Hierarchical Attention Networks for Document Classification
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.