Rootski - Full codebase for rootski.io (without the data)

Overview

breakdown-svg

📣 Welcome to the Rootski codebase!

This is the codebase for the application running at rootski.io.

🗒 Note: You can find information and training on the architecture, ticket board, development practices, and how to contribute on our knowledge base.

Rootski is a full-stack application for studying the Russian language by learning roots.

Rootski uses an A.I. algorithm called a "transformer" to break Russian words into roots. Rootski enriches the word breakdowns with data such as definitions, grammar information, related words, and examples and then displays this information to users for them to study.

How is the Rootski project run? (Hint, get involved here 😃 )

Rootski is developed by volunteers!

We use Rootski as a platform to learn and mentor anyone with an interest in frontend/backend development, developing data science models, data engineering, MLOps, DevOps, UX, and running a business. Although the code is open-source, the license for reuse and redistribution is tightly restricted.

The premise for building Rootski "in the open" is this: possibly the best ways to learn to write production-ready, high quality software is to

  1. explore other high-quality software that is already written
  2. develop an application meant to support a large number of users
  3. work with experienced mentors

For better or worse, it's hard to find code for large software systems built to be hosted in the cloud and used by a large number of customers. This is because virtually all apps that fit this description... are proprietary 🤣 . That makes (1) hard.

(2) can be inaccessible due to the amount of time it takes to write well-written software systems without a team (or mentorship). If you're only interested in a sub-part of engineering, or if you are a beginner, it can be infeasible to build an entire production system on your own. Think of this as working on a personal project... with a bunch of other fun people working on it with you.

Contributors

Onboarded and contributed features :D

  • Eric Riddoch - Been working on Rootski for 3 years and counting!
  • Ryan Gardner - Helping with all of the legal/business aspects and dabbling in development

Friends

Completed a lot of the Rootski onboarding and chat with us in our Slack workspace about miscellanious code questions, careers, advice, etc.

  • Isaac Robbins - Learning and building experience in MLOps and DevOps!
  • Colin Varney - Full-stack python guy. Is working his first full-time software job!
  • Fazleem Baig - MLOps guy. Quite experienced with Python and learning about AWS. Working for an AI startup in Canada.
  • Ayse (Aysha) Arslan - Learning about all things MLOps. Working her first MLE/MLOps job!
  • Sebastian Sanchez - Learning about frontend development.
  • Yashwanth (Yash) Kumar - Finishing up the Georgia Tech online masters in CS.






The Technical Stuff

How to deploy an entire Rootski environment from scratch

Going through this, you'll notice that there are several one-time, manual steps. This is common even for teams with a heavily automated infrastructure-as-code workflow, particularly when it comes to the creation of users and storing of credentials.

Once these steps are complete, all subsequent interactions with our Rootski infrastructure can be done using our infrastructure as code and other automation tools.

1. Create an AWS account and user

  1. Create an IAM user with programmatic access
  2. Install the AWS CLI
  3. Run aws configure --profile rootski and copy the credentials from step (1). Set the region to us-west-2.

🗒 Note: this IAM user will need sufficient permissions to create and access the infrastructure that will be discussed below. This includes creating several types of infrastructure using CloudFormation.

2. Create an SSH key pair

  1. In the AWS console, go to EC2 and create an SSH key pair named rootski.
  2. Download the key pair.
  3. Save the key pair somewhere you won't forget. If the pair isn't already named, I like to rename them and store them at ~/.ssh/rootski/rootski.id_rsa (private key) and ~/.ssh/rootski/rootski.id_rsa.pub (public key).
  4. Create a new GitHub account for a "Machine User". Copy/paste the contents of rootski.id_rsa.pub into any boxes you have to to make this work :D this "machine user" is now authorized to clone the rootski repository!

3. Create several parameters in AWS SSM Parameter Store

Parameter Description
/rootski/ssh/private_key The contents of the private key needed to clone the rootski repository.
/rootski/prod/database_config A stringified JSON object with database connection information (see below)
{
    "postgres_user": "rootski-db-user",
    "postgres_password": "rootski-db-pass",
    "postgres_host": "database.rootski.io",
    "postgres_port": "5432",
    "postgres_db": "rootski-db-database-name"
}

4. Purchase a domain name that happens to be rootski.io

You know, the domain name rootski.io is hard coded in a few places throughout the Rootski infrastructure. It felt wasteful to parameterize this everywhere since... it's unlikely that we will ever change our domain name.

If we ever have a need for this, we can revisit it :D

5. Create an ACM TLS certificate verified with the DNS challenge for *.rootski.io

You'll need to do this in the AWS console. This certificate will allow us to access rootski.io and all of its subdomains over HTTPS. You'll need the ARN of this certificate for a later step.

4. Create the rootski infrastructure

Before running these commands, copy/paste the ARN of the *.rootski.io ACM certificate into the appropriate place in infrastructure/iac/cloudformation/front-end/static-website.yml.

# create the S3 bucket and Route53 hosted zone for hosting the React application as a static site
...

# create the AWS Cognito user pool
...

# create the AWS Lightsail instance with the backend database (simultaneously deploys the database)
...

# deploy the API Gateway and Lambda function
...

5. Deploy the frontend site

make deploy-frontend

DONE!

Owner
Eric
In modern Applied Mathematics, we specialize in algorithms. I'm a data scientist with a strong background in algorithm design and software development.
Eric
Mapping a variable-length sentence to a fixed-length vector using BERT model

Are you looking for X-as-service? Try the Cloud-Native Neural Search Framework for Any Kind of Data bert-as-service Using BERT model as a sentence enc

Han Xiao 11.1k Jan 01, 2023
Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation This is the implementaion of our paper: Bridging the

hezw.tkcw 20 Dec 12, 2022
Accurately generate all possible forms of an English word e.g "election" --> "elect", "electoral", "electorate" etc.

Accurately generate all possible forms of an English word Word forms can accurately generate all possible forms of an English word. It can conjugate v

Dibya Chakravorty 570 Dec 31, 2022
Contains the code and data for our #ICSE2022 paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences"

CodeFill This repository contains the code for our paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Namin

Software Analytics Lab 11 Oct 31, 2022
English loanwords in the world's languages

Wiktionary as CLDF Content cldf1 and cldf2 contain cldf-conform data sets with a total of 2 377 756 entries about the vocabulary of all 1403 languages

Viktor Martinović 3 Jan 14, 2022
Reformer, the efficient Transformer, in Pytorch

Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH

Phil Wang 1.8k Dec 30, 2022
Chinese version of GPT2 training code, using BERT tokenizer.

GPT2-Chinese Description Chinese version of GPT2 training code, using BERT tokenizer or BPE tokenizer. It is based on the extremely awesome repository

Zeyao Du 5.6k Jan 04, 2023
TaCL: Improve BERT Pre-training with Token-aware Contrastive Learning

TaCL: Improve BERT Pre-training with Token-aware Contrastive Learning

Yixuan Su 26 Oct 17, 2022
DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task

DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task。涵盖68个领域、共计916万词的专业词典知识库,可用于文本分类、知识增强、领域词汇库扩充等自然语言处理应用。

liuhuanyong 357 Dec 24, 2022
Code for CodeT5: a new code-aware pre-trained encoder-decoder model.

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation This is the official PyTorch implementation

Salesforce 564 Jan 08, 2023
Signature remover is a NLP based solution which removes email signatures from the rest of the text.

Signature Remover Signature remover is a NLP based solution which removes email signatures from the rest of the text. It helps to enchance data conten

Forges Alterway 8 Jan 06, 2023
I label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive

I label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive. Obstacles like sentence negation, sarcasm, terseness, language ambiguity, and many others

1 Jan 13, 2022
문장단위로 분절된 나무위키 데이터셋. Releases에서 다운로드 받거나, tfds-korean을 통해 다운로드 받으세요.

Namuwiki corpus 문장단위로 미리 분절된 나무위키 코퍼스. 목적이 LM등에서 사용하기 위한 데이터셋이라, 링크/이미지/테이블 등등이 잘려있습니다. 문장 단위 분절은 kss를 활용하였습니다. 라이선스는 나무위키에 명시된 바와 같이 CC BY-NC-SA 2.0

Jeong Ukjae 16 Apr 02, 2022
A tool helps build a talk preview image by combining the given background image and talk event description

talk-preview-img-builder A tool helps build a talk preview image by combining the given background image and talk event description Installation and U

PyCon Taiwan 4 Aug 20, 2022
customer care chatbot made with Rasa Open Source.

Customer Care Bot Customer care bot for ecomm company which can solve faq and chitchat with users, can contact directly to team. 🛠 Features Basic E-c

Dishant Gandhi 23 Oct 27, 2022
Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".

Yu Zhang 50 Nov 08, 2022
Pattern Matching in Python

Pattern Matching finalmente chega no Python 3.10. E daí? "Pattern matching", ou "correspondência de padrões" como é conhecido no Brasil. Algumas pesso

Fabricio Werneck 6 Feb 16, 2022
Pretty-doc - Composable text objects with python

pretty-doc from __future__ import annotations from dataclasses import dataclass

Taine Zhao 2 Jan 17, 2022
BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions

BERTopic BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable

Maarten Grootendorst 3.6k Jan 07, 2023
Multilingual text (NLP) processing toolkit

polyglot Polyglot is a natural language pipeline that supports massive multilingual applications. Free software: GPLv3 license Documentation: http://p

RAMI ALRFOU 2.1k Jan 07, 2023