1378 Open source projects that are alternatives of or similar to Deep Generative Models For Natural Language Processing

Default English stopword lists from many different sources

《机器翻译：基础与模型》肖桐 朱靖波 著 - Machine Translation: Foundations and Models

shinystan R package and ShinyStan GUI

A library for performing and validating complex filters from a client (e.g. smart filters)

Python script to download public Tweets from a given Twitter account into a format suitable for AI text generation.

State of the Art Natural Language Processing

Python library for Natural Language Preprocessing (NLPre)

Tensorflow implementation of CVPR 2018 paper "Disentangled Person Image Generation"

Topic Modelling for Humans

My completed implementation solutions for CS224N 2019

OpenAI GPT2 pre-training and sequence prediction implementation in Tensorflow 2.0

📖 A curated list of resources dedicated to Natural Language Processing (NLP)

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

The Cyber Swiss Army Knife - a web app for encryption, encoding, compression and data analysis

SLING - A natural language frame semantics parser

Global-Locally Self-Attentive Dialogue State Tracker

My (slightly modified) Keras implementation of the Recurrent Convolutional Neural Network (RCNN) described here: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745.

Fast, direct and static typed parsing of JSON with C++

A privacy preserving NLP framework

Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

Movie Ratings Synchronization with Python

❤️Emotional First Aid Dataset, 心理咨询问答、聊天机器人语料库

PyTorch code for Learning Cooperative Visual Dialog Agents using Deep Reinforcement Learning

📅🇨🇳 中国法定节假日数据 自动每日抓取国务院公告

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Bayesian Evolutionary Analysis by Sampling Trees

Cross Platform C# web crawler framework built for speed and flexibility. Please star this project! +1.

A frame-semantic parsing system based on a softmax-margin SegRNN.

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Repository for paper "SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference"

This is a pix2pix demo that learns from pose and translates this into a human. A webcam-enabled application is also provided that translates your pose to the trained pose. Everybody dance now !

Style transfer using deep convolutional neural nets

Collection of stats, modeling, and data science tools in Python and R.

How to build RNNs and LSTMs from scratch with NumPy.

Learning Chinese Character style with conditional GAN

Simple State-of-the-Art BERT-Based Sentence Classification with Keras / TensorFlow 2. Built with HuggingFace's Transformers.

Python wrapper for evaluating summarization quality by ROUGE package

基于深度学习的自然语言处理库

fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

Data and software for building the ACL Anthology.

PyTorch implementation of "Lagging Inference Networks and Posterior Collapse in Variational Autoencoders" (ICLR 2019)

Set up the CTRL text-generating model on Google Compute Engine with just a few console commands.

Text vectorization tool to outperform TFIDF for classification tasks

Embedding, NMT, Text_Classification, Text_Generation, NER etc.

Important paper implementations for Question Answering using PyTorch

CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).

Collection of software components for natural language processing (NLP) based on the Apache UIMA framework.

a sklearn wrapper for Google's BERT model

R package providing annotators and a normalized data model for natural language processing

⚡️A Lightweight NLP Data Loader for All Deep Learning Frameworks in Python

This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera

QANTA Quiz Bowl AI

A pytorch implementation of MINE(Mutual Information Neural Estimation)

Examine two sentences and determine whether they have the same meaning.

Automatically extract chemical information from scientific documents

Web scrapping and related analytics using Python tools

It is a question-generator model. It takes text and an answer as input and outputs a question.

A Library to parse natural language in pure Clojure and ClojureScript