text_classification - all kinds of text classificaiton models and more with deep learning

  •        177

the purpose of this repository is to explore text classification methods in NLP with deep learning. sentence similarity project has been released you can check it if you like.

https://github.com/brightmart/text_classification

Tags
Implementation
License
Platform

   




Related Projects

NeuralNLP-NeuralClassifier - An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

  •    Python

NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. It is built on PyTorch. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature. Detail configurations and explanations see Configuration.

cnn-text-classification-tf-chinese - CNN for Chinese Text Classification in Tensorflow

  •    Python

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

DeText - A Deep Neural Text Understanding Framework for Ranking and Classification Tasks

  •    Python

DeText is a Deep Text understanding framework for NLP related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks.

cnn-text-classification-tf - Convolutional Neural Network for Text Classification in Tensorflow

  •    Python

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

tf-rnn-attention - Tensorflow implementation of attention mechanism for text classification tasks.

  •    Python

Tensorflow implementation of attention mechanism for text classification tasks. Inspired by "Hierarchical Attention Networks for Document Classification", Zichao Yang et al. (http://www.aclweb.org/anthology/N16-1174).


neuralmonkey - An open-source tool for sequence learning in NLP built on TensorFlow.

  •    Python

The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

CNN-for-Sentence-Classification-in-Keras - Convolutional Neural Networks for Sentence Classification in Keras

  •    Python

Convolutional Neural Networks for Sentence Classification in Keras

Multilabel-timeseries-classification-with-LSTM - Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks

  •    Jupyter

Tensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Recurrent Neural Networks. MIMIC-III dataset can possibly be use to train and test the model. Beware this is not the data set used by the authors of the paper.

zhihu-text-classification - [2017知乎看山杯 多标签 文本分类] ye组(第六名) 解题方案

  •    Jupyter

和 creat_batch_data.py 相同,只是对 content 部分进行句子划分。用于分层模型。 划分句子长度: wd_title_len = 30, wd_sent_len = 30, wd_doc_len = 10.(即content划分为10个句子,每个句子长度为30个词) ch_title_len = 52, ch_sent_len = 52, ch_doc_len = 10. 不划分句子: wd_title_len = 30, wd_content_len = 150. ch_title_len = 52, ch_content_len = 300.

limdu - Machine-learning for Node.js

  •    Javascript

Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

fastText - Library for fast text representation and classification.

  •    HTML

fastText is a library for efficient learning of word representations and sentence classification. You can find answers to frequently asked questions on our website.

MSDNet - Multi-Scale Dense Networks for Resource Efficient Image Classification (ICLR 2018 Oral)

  •    Lua

This repository provides the code for the paper Multi-Scale Dense Networks for Resource Efficient Image Classification. This paper studies convolutional networks that require limited computational resources at test time. We develop a new network architecture that performs on par with state-of-the-art convolutional networks, whilst facilitating prediction in two settings: (1) an anytime-prediction setting in which the network's prediction for one example is progressively updated, facilitating the output of a prediction at any time; and (2) a batch computational budget setting in which a fixed amount of computation is available to classify a set of examples that can be spent unevenly across 'easier' and 'harder' examples.

food-101-keras - Food Classification with Deep Learning in Keras / Tensorflow

  •    Jupyter

If you are reading this on GitHub, the demo looks like this. Please follow the link below to view the live demo on my blog. Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. determining whether a picture is that of a dog or cat.

trashnet - Dataset of images of trash; Torch-based CNN for garbage image classification

  •    Lua

Code (only for the convolutional neural network) and dataset for mine and Mindy Yang's final project for Stanford's CS 229: Machine Learning class. Our paper can be found here. The convolutional neural network results on the poster are dated since we continued working after the end of the quarter and were able to achieve around 75% test accuracy (with 70/13/17 train/val/test split) after changing the weight initialization to the Kaiming method. The pictures were taken by placing the object on a white posterboard and using sunlight and/or room lighting. The pictures have been resized down to 512 x 384, which can be changed in data/constants.py (resizing them involves going through step 1 in usage). The devices used were Apple iPhone 7 Plus, Apple iPhone 5S, and Apple iPhone SE.

text-classification-models-tf - Tensorflow implementations of Text Classification Models.

  •    Python

Tensorflow implementation of Text Classification Models. Semi-supervised text classification(Transfer learning) models are implemented at [dongjun-Lee/transfer-learning-text-tf].

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

darkflow - Translate darknet to tensorflow

  •    Python

Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full and yolo-tiny of v1.0, tiny-yolo-v1.1 of v1.1 and yolo, tiny-yolo-voc of v2.

XNOR-Net - ImageNet classification using binary Convolutional Neural Networks

  •    Lua

This is the Torch 7.0 implementation of XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. This software is implemented on top of the implementation of ImageNet-multiGPU and has all the same requirements.

applied-deep-learning-resources - A collection of research articles, blog posts, slides and code snippets about deep learning in applied settings

  •    

A collection of research articles, blog posts, slides and code snippets about deep learning in applied settings. Including trained models and simple methods that can be used out of the box. Mainly focusing on Convolutional Neural Networks (CNN) but Recurrent Neural Networks (RNN), deep Q-Networks (DQN) and other interesting architectures will also be listed. ImageNet is the most important image classification and localization competition. Other data sets with results can be found from here: "Discover the current state of the art in objects classification." [link].

ResNeXt - Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks

  •    Lua

This repository contains a Torch implementation for the ResNeXt algorithm for image classification. The code is based on fb.resnet.torch. ResNeXt is a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width.






We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.