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py_ml_utils

Some small utility modules to help with pandas, numpy and sklearn usage in other projects

py_word_suggest

pyticli

Python Temporal Intervals Common Language Interface

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-lambdapack_server

Serving PyTorch Models on AWS Lambda

pytorch-pretrained-bert

A PyTorch implementation of Google AI's BERT model provided with Google's pre-trained models, examples and utilities.

pytorch-seq2seq-intent-parsing

Intent parsing and slot filling in PyTorch with seq2seq + attention

pytorch_2_keras

Convert trained PyTorch models to Keras, and the other way around

pytorch_chatbot

A Marvelous ChatBot implement using PyTorch.

pytorch_learn

学习pytorch，ChineseCorpus是一个中文的实体识别程序

pytorch_multi_head_selection_re

BERT + reproduce "Joint entity recognition and relation extraction as a multi-head selection problem" for Chinese and English IE

pytorch_rvae_paraphrase

Recurrent Variational Autoencoder that generates sequential data implemented with pytorch

pytorch_tensorboard

TensorBoard usage from PyTorch with MNIST example

pywfm

pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM

pywit

Python library for Wit.ai

pyzimudog

用python爬取字幕库的的字幕信息

qa

TensorFlow Models for the Stanford Question Answering Dataset

qa-2

A simple QA system for English factual questions in Python.

qa-4

Deep Learning for Question Answering

qa-apache

A Question Answering system built on top of the Apache UIMA framework.

qa-chinese_2

A simple task for learning to process the text data

qa-cnn-lstm

tensorflow and theano cnn code for insurance QA(question Answer matching)

qa-demo

Question Answering Demo

qa-hard-em

An original implementation of EMNLP 2019, "A Discrete Hard EM Approach for Weakly Supervised Question Answering"

qa-it

Non-referential It on Question Answer Pairs

qa-reader

a qa system based on machine reading comprehension.

qa-snake_chinese

基于多搜索引擎和深度学习技术的自动问答

qa-squad2.0

Extractive Question Answering on SQuAD 2.0

qa-survey

北航大数据高精尖中心研究张日崇团队对问答系统的调研。包括知识图谱问答系统（KBQA）和文本问答系统（TextQA），每类系统分别对学术界和工业界进行调研。

qa-system

Implemented a QA System. This is the code for the NLPCC-ICCPOL shared task "Open Domain Question Answering."