Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

Chinese-Text-Classification，Tensorflow CNN（卷积神经网络）实现的中文文本分类。QQ群：522785813，微信群二维码：http://www.tensorflownews.com/

Tensorflow Implementation of Convolutional Neural Network for Relation Extraction (COLING 2014, NAACL 2015)

Character-level Convolutional Neural Networks for text classification in PyTorch

TextClf ：基于Pytorch/Sklearn的文本分类框架，包括逻辑回归、SVM、TextCNN、TextRNN、TextRCNN、DRNN、DPCNN、Bert等多种模型，通过简单配置即可完成数据处理、模型训练、测试等过程。

整理记录本人担任课程助教设计的四个机器学习实验，主要涉及简单的线性回归、朴素贝叶斯分类器、支持向量机、CNN做文本分类。内附实验指导书、讲解PPT、参考代码，欢迎各位码友讨论交流。

📚 Text Classification Library with Keras

A PyTorch CNN for classifying the sentiment of movie reviews, based on the paper "Convolutional Neural Networks for Sentence Classification" by Yoon Kim (2014).

Pytorch implementation of the paper Deep learning for extreme multi-label text classification

pyTorch-text-classification

🎒 基于CNN实现的文本分类应用

运用cnn + highway network网络结构中文文本分类

A Chainer implementation of a Convolutional Network model for relation classification in the SemEval Task 8 dataset. This model performs Multi-Way Classification of Semantic Relations Between Pairs of Nominals in the SemEval 2010 task 8 dataset.

Repository of state of the art text/documentation classification algorithms in Pytorch.

This repository contains Sentiment Classification, Word Level Text Generation, Character Level Text Generation and other important codes/notes on NLP. Python and Keras are used for implementation.

Deep Learning for Toxic Comment Classification

PyTorch Sentence Classifier (CNN RNN)

mobile speech text classify using CNN and LSTM with pytorch.

simple CNN text classification with Keras

PyTorch sentiment classifier served with Flask, deployed on Google Cloud Run

Deep Learning architectures for the fascinating task of sentence selection for QA systems.

CNN for hierarchical text classification

Using N-Gram Multichannel CNN for Sentiment Analysis

Weather-Flight-Bot, a combined model of semantics slot-filling and intent detection powered by neural network (LSTM and CNN layers).

Text-based Geolocation Prediction of Social Media Users with Neural Networks

Text classification using GloVe embeddings, CNN and stacked bi-directional LSTM with Max K Pooling.

Assignments and projects for the course: Speech and Natural Language Processing

A Shared Task on Contextual Emotion Detection in Text (done in exactly two days; one before mid-eval and one before end-eval)

Used Tensorflow and Keras Framework