Jiagu深度学习自然语言处理工具 知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类

⚡️Curated list of resources for the development and applications of blockchain.

Bitcoin full node in C#

基于Pytorch和torchtext的自然语言处理深度学习框架。

An awesome curated list of Cryptoeconomic research and learning materials

快速中文分词分析word segmentation

Part-of-speech utilities for node.js based on the WordNet database.

fastHan是基于fastNLP与pytorch实现的中文自然语言处理工具，像spacy一样调用方便。

A next generation decentralised Ethereum proof of stake network and pool, currently in beta and built to be compatible with Ethereum 2.0 and the Beacon Chain.

Provas e gabaritos da POSCOMP, sem marcação das respostas 📚

TailPOS an Offline First Open Source POS for ERPNext

😋本项目旨在通过Tensorflow基于BiLSTM+CRF实现中文分词、词性标注、命名实体识别（NER）。

比特币的python简易实现，主要介绍比特币底层技术。如Base58编码、椭圆加密算法、MerkleTree、P2P对等网络、RPC通信、UTXO、虚拟机、DHT、DAG、链上数据的持久化存储等。

A Python wrapper of the Yandex Mystem 3.1 morphological analyzer (http://api.yandex.ru/mystem). The original tool is shipped as a binary and this library makes it easy to integrate it in Python projects. Let us know in the issues if you would like to be involved into the developments or maintenance of this project. If you have any fix or suggestion, please make a pull request. We are very open to accepting any contributions.

MONPA 罔拍是一個提供正體中文斷詞、詞性標註以及命名實體辨識的多任務模型

top 1% solution to toxic comment classification challenge on Kaggle.

Minimarket Point Of Sales (POS) software writen in C++ with Qt framework

Point of Sales with inventory management system

TUTA Point of Sale Using Laravel & Angularjs Framework

ESC/POS (thermal, receipt) printing for Flutter & Dart

Perangkat lunak open source yang dikembangkan khusus untuk bidang usaha ritel, grosir, toko bangunan, toko komputer, toko buku, counter hp, pos, point of sale, kasir dan bidang usaha lainnya yang sejenis.

Utilities to read ESC/POS print data

Chinese & English Cws Pos Ner Entity Recognition implement using CNN bi-directional lstm and crf model with char embedding.基于字向量的CNN池化双向BiLSTM与CRF模型的网络，可能一体化的完成中文和英文分词，词性标注，实体识别。主要包括原始文本数据，数据转换,训练脚本,预训练模型,可用于序列标注研究.注意：唯一需要实现的逻辑是将用户数据转化为序列模型。分词准确率约为93%，词性标注准确率约为90%，实体标注（在本样本上）约为85%。

Türk bankaları için sanal pos paketi (PHP)

A simple Lightning ⚡ point-of-sale system, powered by Lightning Charge

🏬 Point-of-sale and inventory tracking for small-scale retail stores. Easily manage inventory, purchases, sales, and damaged and/or expired products.

Papers and Book to look at when starting NLP 📚

PHP wrapper for the Stanford Natural Language Processing library. Supports POSTagger and CRFClassifier.