Displaying 1 to 10 from 10 results

Uwazi - Web-based, open-source solution for building and sharing document collections

  •    Javascript

There are important stories within your documents. Uwazi helps you tell them. Uwazi is a free, open-source solution for organising, analysing and publishing your documents. Uwazi is configured to run correctly with its default values. There is no need to change or reconfigure these values.

hierarchical-attention-networks - Document classification with Hierarchical Attention Networks in TensorFlow

  •    Python

Implementation of document classification model described in Hierarchical Attention Networks for Document Classification (Yang et al., 2016).I am getting 65% accuracy on a dev set (16% of data) after 3 epochs. Results reported in the paper are 71% on Yelp'15. No systemic hyperparameter optimization was performed.




text-classification-keras - 📚 Text Classification Library with Keras

  •    Python

A high-level text classification library implementing various well-established models. With a clean and extendable interface to implement custom architectures. The [full] will additionally install TensorFlow, Spacy, and Deep Plots. Choose this if you want to get started right away.

Hierarchical-Attention-Network - Implementation of Hierarchical Attention Networks in PyTorch

  •    Jupyter

We know that documents have a hierarchical structure, words combine to form sentences and sentences combine to form documents. We can try to learn that structure or we can input this hierarchical structure into the model and see if it improves the performance of existing models. This paper exploits that structure to build a classification model. This is a (close) implementation of the model in PyTorch.

Text-Classification - PyTorch implementation of some text classification models (HAN, fastText, BiLSTM-Attention, TextCNN, Transformer) | 文本分类

  •    Python

PyTorch re-implementation of some text classificaiton models. Train the following models by editing model_name item in config files (here are some example config files). Click the link of each for details.

EdPaper - Helps you organizing your paperwork

  •    PHP

EdPaper is a laravel-php driven Document organizer made for PDFs. They are stored on your server (unencrypted, beware !) You'll need composer and PHP (5 or 7). Rename .env.example to .env and run php artisan key:generate from the app's root path.


TextClassificationApp - Building and Deploying A Serverless Text Classification Web App

  •    Jupyter

In this project, over a series of blog posts I'll be buidling a model document classification, also known as text classification and deploying the model as part of a web application to predict the topic of research papers from their abstract. In the first blog post I will be working with the Scikit-learn library and an imbalanced dataset (corpus) that I will create from summaries of papers published on arxiv. The topic of each paper is already labeled as the category therefore alleviating the need for me to label the dataset. The imbalance in the dataset will be caused by the imbalance in the number of samples in each of the categories we are trying to predict. Imbalanced data occurs quite frequently in classification problems and makes developing a good model more challenging. Often times it is too expensive or not possible to get more data on the classes that have to few samples. Developing strategies for dealing with imbalanced data is therefore paramount for creating a good classification model. I will cover some of the basics of dealing with imbalanced data using the Imbalance-Learn library as well as building a Naive Bayes classifier and Support Vector Machine using from Scikit-learn. I will also over the basics of term frequency-inverse document frequency and visualizing it using the Plotly library.

EdPaper - Helps you organizing your paperwork

  •    PHP

EdPaper is a laravel-php driven Document organizer made for PDFs. They are stored on your server (unencrypted, beware !) You'll need composer and PHP (5 or 7). Rename .env.example to .env and run php artisan key:generate from the app's root path.






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