Lingtrain Alignment Studio is an ML based app for texts alignment on different languages.

Overview

Lingtrain Alignment Studio

asd

Intro

Lingtrain Alignment Studio is the ML based app for accurate texts alignment on different languages.

  • Extracts parallel corpora from two texts.
  • Makes the formatted parallel book from it with sentence highlightning.

Models

Automated alignment process relies on the sentence embeddings models. Embeddings are multidimensional vectors of a special kind which are used to calculate a distance between the sentences. You can also plug your own model using the interface described in models directory. Supported languages list depend on the selected backend model.

  • distiluse-base-multilingual-cased-v2
    • more reliable and fast
    • moderate weights size — 500MB
    • supports 50+ languages
    • full list of supported languages can be found in this paper
  • LaBSE (Language-agnostic BERT Sentence Embedding)
    • can be used for rare languages
    • pretty heavy weights — 1.8GB
    • supports 100+ languages
    • full list of supported languages can be found here

Running on local machine

You can run the application on your computer using docker.

  1. Make sure that docker is installed by typing the docker version command in your console.

  2. Images configured to run locally are available on Docker Hub.

  3. Run the following commads in your console:

    • docker pull lingtrain/aligner:v6
    • docker run -v C:\app\data:/app/data -v C:\app\img:/app/static/img -p 80:80 lingtrain/aligner:v6
    • Use lingtrain/aligner:v6-labse for LaBSE version (109 languages).
  4. App will be available in your browser on the localhost address.

  5. If you need to run the container on another port (e.g. localhost:8081):

    • Change the API_URL parameter in config.js
    • Rebuild the docker container
    • Start it with changed -p parameter (e.g. -p 8081:80)

Running in development mode

Clone this repo on your machine.

Backend

Flask/uwsgi backend REST API service. It's pretty simple and contains all the alignment logic.

cd /be python main.py

Frontend

SPA. Vue + vuex + vuetify. UI for managing alignment process using BE and a tool for translators to edit processing documents.

cd /fe

Setup

npm install

Compile and run with hot-reloads for development

npm run serve

Feedback

You can crate an issue or send me a message in telegram: @averkij

License

This work is licensed under a Attribution-NonCommercial-NoDerivatives 4.0 International license. See LICENSE.

Creative Commons License

Owner
Sergei Averkiev
Software Engineer. Eager to learn languages and machine learning approaches. Live in Moscow.
Sergei Averkiev
GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

Generator of Rad Names from Decent Paper Acronyms

264 Nov 08, 2022
MLOps pipeline project using Amazon SageMaker Pipelines

This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drif

AWS Samples 3 Sep 16, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just

wenqi 2 Jun 26, 2022
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

SageMaker Python SDK SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the S

Amazon Web Services 1.8k Jan 01, 2023
Predicting diabetes over a five year period using logistic regression and the Pima First-Nation dataset

Diabetes This script uses the Pima First Nations dataset to create a model to predict whether or not an individual will develop Diabetes Mellitus Type

1 Mar 28, 2022
Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models.

Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. Feature-engine's transformers follow scikit-learn's functionality wit

Soledad Galli 33 Dec 27, 2022
Machine Learning e Data Science com Python

Machine Learning e Data Science com Python Arquivos do curso de Data Science e Machine Learning com Python na Udemy, cliqe aqui para acessá-lo. O prin

Renan Barbosa 1 Jan 27, 2022
BigDL: Distributed Deep Learning Framework for Apache Spark

BigDL: Distributed Deep Learning on Apache Spark What is BigDL? BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can w

4.1k Jan 09, 2023
Python/Sage Tool for deriving Scattering Matrices for WDF R-Adaptors

R-Solver A Python tools for deriving R-Type adaptors for Wave Digital Filters. This code is not quite production-ready. If you are interested in contr

8 Sep 19, 2022
Random Forest Classification for Neural Subtypes

Random Forest classifier for neural subtypes extracted from extracellular recordings from human brain organoids.

Michael Zabolocki 1 Jan 31, 2022
Self Organising Map (SOM) for clustering of atomistic samples through unsupervised learning.

Self Organising Map for Clustering of Atomistic Samples - V2 Description Self Organising Map (also known as Kohonen Network) implemented in Python for

Franco Aquistapace 0 Nov 16, 2021
A collection of Scikit-Learn compatible time series transformers and tools.

tsfeast A collection of Scikit-Learn compatible time series transformers and tools. Installation Create a virtual environment and install: From PyPi p

Chris Santiago 0 Mar 30, 2022
A simple example of ML classification, cross validation, and visualization of feature importances

Simple-Classifier This is a basic example of how to use several different libraries for classification and ensembling, mostly with sklearn. Example as

Rob 2 Aug 25, 2022
Diabetes Prediction with Logistic Regression

Diabetes Prediction with Logistic Regression Exploratory Data Analysis Data Preprocessing Model & Prediction Model Evaluation Model Validation: Holdou

AZİZE SULTAN PALALI 2 Oct 23, 2021
Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)

Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)

141 Dec 27, 2022
stability-selection - A scikit-learn compatible implementation of stability selection

stability-selection - A scikit-learn compatible implementation of stability selection stability-selection is a Python implementation of the stability

185 Dec 03, 2022
Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Highly interpretable, sklearn-compatible classifier based on decision rules This is a scikit-learn compatible wrapper for the Bayesian Rule List class

Tamas Madl 482 Nov 19, 2022
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training

MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th

MosaicML 2.8k Jan 06, 2023
WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can b

Shigang Li 6 Jun 18, 2022