MILES is a multilingual text simplifier inspired by LSBert - A BERT-based lexical simplification approach proposed in 2018. Unlike LSBert, MILES uses the bert-base-multilingual-uncased model, as well as simple language-agnostic approaches to complex word identification (CWI) and candidate ranking.

Overview

MILES

Multilingual Lexical Simplifier
Explore the docs »

Read LSBert Paper · Report Bug · Request Feature

About The Project

MILES is a multilingual text simplifier inspired by LSBert - A BERT-based lexical simplification approach proposed in 2018. Unlike LSBert, MILES uses the bert-base-multilingual-uncased model, as well as simple language-agnostic approaches to complex word identification (CWI) and candidate ranking. MILES currently supports 22 languages: Arabic, Bulgarian, Catalan, Czech, Danish, Dutch, English, Finnish, French, German, Hungarian, Indonesian, Italian, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, and Ukrainian.

As a result of not using any language-specific resources (WordNets, POS taggers, parallel corpora, etc.), MILES does not always offer synonymous substitutions for complex words. Although almost always simpler than the original, selected substitutions may alter the meaning of the text. Please keep this in mind, and feel free to download and tailor MILES to a language of your choosing!

Prerequisites

FastText Embeddings

It is recommended that fastText embeddings are downloaded for your target language/s. These will be used by MILES to make notably more accurate simplifications. To install fastText embeddings for MILES, download the .vec embeddings for you target language here. Once done, place the .vec file in simplifier/embeddings/ before running the key vector generation script with the ISO 639-1 code for the selected language:

python simplifier/embeddings/gen_keyed_vectors.py <ISO 639-1 code>

Usage

Flask App

MILES simplifications can be done using either a simple Flask app provided or the command line. To start using the Flask app, run app.py with ISO 639-1 language code:

python app.py -l <ISO 639-1 code>

Once running, open 127.0.0.1 in your browser and start simplifying!

flask app

Command Line

If you would prefer to use the command line, there are a couple of options available:

  1. Simplifying sentences:

    python simplify.py -t <sentence> -l <ISO 639-1 code>
  2. Simplifying text files:

    python simplify.py -f <text_file> -l <ISO 639-1 code>

Note: If no language code is provided, text will be simplified assuming it's English. The default language can be changed in simplifier/config.py.

Framework

flowchart

Roadmap

See the open issues for a list of proposed features (and known issues).

Contact

If you have any questions or concerns, message me on LinkedIn or email me at [email protected].

Owner
Kane
MSc Computer Science by Research student. Areas of interest include text simplification and other areas of NLP.
Kane
Shared code for training sentence embeddings with Flax / JAX

flax-sentence-embeddings This repository will be used to share code for the Flax / JAX community event to train sentence embeddings on 1B+ training pa

Nils Reimers 23 Dec 30, 2022
Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision Training Efficiency We show the training efficiency of our DSLP model b

Chenyang Huang 37 Jan 04, 2023
Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables

Mortgage-Application-Analysis Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables: age, in

1 Jan 29, 2022
Behavioral Testing of Clinical NLP Models

Behavioral Testing of Clinical NLP Models This repository contains code for testing the behavior of clinical prediction models based on patient letter

Betty van Aken 2 Sep 20, 2022
天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

zxx飞翔的鱼 751 Dec 30, 2022
Speech Recognition for Uyghur using Speech transformer

Speech Recognition for Uyghur using Speech transformer Training: this model using CTC loss and Cross Entropy loss for training. Download pretrained mo

Uyghur 11 Nov 17, 2022
A combination of autoregressors and autoencoders using XLNet for sentiment analysis

A combination of autoregressors and autoencoders using XLNet for sentiment analysis Abstract In this paper sentiment analysis has been performed in or

James Zaridis 2 Nov 20, 2021
Mastering Transformers, published by Packt

Mastering Transformers This is the code repository for Mastering Transformers, published by Packt. Build state-of-the-art models from scratch with adv

Packt 195 Jan 01, 2023
Tokenizer - Module python d'analyse syntaxique et de grammaire, tokenization

Tokenizer Le Tokenizer est un analyseur lexicale, il permet, comme Flex and Yacc par exemple, de tokenizer du code, c'est à dire transformer du code e

Manolo 1 Aug 15, 2022
[Preprint] Escaping the Big Data Paradigm with Compact Transformers, 2021

Compact Transformers Preprint Link: Escaping the Big Data Paradigm with Compact Transformers By Ali Hassani[1]*, Steven Walton[1]*, Nikhil Shah[1], Ab

SHI Lab 367 Dec 31, 2022
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

Amazon Web Services - Labs 1.1k Dec 27, 2022
Asr abc - Automatic speech recognition(ASR),中文语音识别

语音识别的简单示例,主要在课堂演示使用 创建python虚拟环境 在linux 和macos 上验证通过 # 如果已经有pyhon3.6 环境,跳过该步骤,使用

LIyong.Guo 8 Nov 11, 2022
Korean stereoypte detector with TUNiB-Electra and K-StereoSet

Korean Stereotype Detector Korean stereotype sentence classifier using K-StereoSet with TUNiB-Electra Web demo you can test this model easily in demo

Sae_Chan_Oh 11 Feb 18, 2022
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context This repository contains the code in both PyTorch and TensorFlow for our paper

Zhilin Yang 3.3k Dec 28, 2022
Outreachy TFX custom component project

Schema Curation Custom Component Outreachy TFX custom component project This repo contains the code for Schema Curation Custom Component made as a par

Robert Crowe 5 Jul 16, 2021
Implementation of Natural Language Code Search in the project CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

CodeBERT-Implementation In this repo we have replicated the paper CodeBERT: A Pre-Trained Model for Programming and Natural Languages. We are interest

Tanuj Sur 4 Jul 01, 2022
MRC approach for Aspect-based Sentiment Analysis (ABSA)

B-MRC MRC approach for Aspect-based Sentiment Analysis (ABSA) Paper: Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extracti

Phuc Phan 1 Apr 05, 2022
DziriBERT: a Pre-trained Language Model for the Algerian Dialect

DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect.

117 Jan 07, 2023
Share constant definitions between programming languages and make your constants constant again

Introduction Reconstant lets you share constant and enum definitions between programming languages. Constants are defined in a yaml file and converted

Natan Yellin 47 Sep 10, 2022
Voilà turns Jupyter notebooks into standalone web applications

Rendering of live Jupyter notebooks with interactive widgets. Introduction Voilà turns Jupyter notebooks into standalone web applications. Unlike the

Voilà Dashboards 4.5k Jan 03, 2023