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

  •        26

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

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.

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


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.

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.

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.

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.

ConvNetJS - Javascript implementation of Neural networks

  •    Javascript

ConvNetJS is a Javascript implementation of Neural networks, It currently supports Common Neural Network modules, Classification (SVM/Softmax) and Regression (L2) cost functions, A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations), Ability to specify and train Convolutional Networks that process images, An experimental Reinforcement Learning module, based on Deep Q Learning.

pycm - Multi-class confusion matrix library in Python

  •    Python

PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. threshold is added in version 0.9 for real value prediction.