mousamohammed Goto Github PK

abstractive-text-summarization

bert-summarizer

Text Summarization with Pretrained Encoders

bertsum

My fork of BerSum, altered to summarize Arxiv Papers

d2l-en

Interactive deep learning book with code, math, and discussions. Available in multi-frameworks.

dplp

A RST Parser with a trained model

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

german-traffic-sign-classification

Deep Learning algorithm for Image recognition challenge

german-traffic-sign-classification-using-tensorflow

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.

german-traffic-sign-recognition

Deep neural networks and convolutional neural networks to classify German traffic signs.

ict

nla's workflow so dope u'd be jealousss

image-classification-with-logistic-regression

linear-regression-with-pytorch

matchsum

Code for ACL 2020 paper: "Extractive Summarization as Text Matching"

material

An NLP project in Summarization, Columbia University

neu-compression-sum

Joint Extraction & Compression text Summarization

nlp-abstractivesummarizer

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.

nlp_summarization

The results of toy example summaries and DUC corpus evaluated with different python libraries based on ROUGE metric

papers

summary of ML papers I've read

pytorch-seq2seq

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

rouge-python

Python text summarizer evaluation tool using ROUGE methods

sidenet

SideNet: Neural Extractive Summarization with Side Information

text-classification-using-rnn

text-generation-using-deep-learning

text-summarization-using-deep-learning

textclassifier

Text classifier for Hierarchical Attention Networks for Document Classification