MILES VS gensim

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. Although not all have been tested, MILES should support 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.

Recently, I started working on another simplifier called MILES. MILES is loosely inspired by LSBert — another lexical simplifier that uses the large BERT uncased model to find substitutions for complex words. MILES works in a very similar way, however, it instead makes use of the multilingual BERT model, as well as fully language-agnostic methods for complex word identification and substitution ranking. As a result, MILES can (in theory) support a multitude of different languages. The GitHub repository can be found here, and below I've included an example text simplified by MILES, as well as an overview of the framework.

This is our optimization problem. Now, we hope that you have an idea of what our goal is. Luckily for us, this is already implemented in a Python module called gensim. Yes, these guys are brilliant in natural language processing and we will make use of it. 🤝

Try LDA as implemented in gemsin https://github.com/RaRe-Technologies/gensim

Standout python NLP libraries include Spacy and Gensim, as well as pre-trained model availability in Hugginface. These libraries have widespread use in and support from industry and it shows. Spacy has best-in-class methods for pre-processing text for further applications. Gensim helps you manage your corpus of documents, and contains a lot of different tools for solving a common industry task, topic modeling.

Here we have to install the gensim library in a jupyter notebook to be able to use it in our project, consider the code below;

Nice work! I wonder if you're going the same challenges that gensim had for being generic in summarization.

For context:

> Despite its general-sounding name, the module will not satisfy the majority of use cases in production and is likely to waste people's time.

https://github.com/RaRe-Technologies/gensim/wiki/Migrating-f...

TextRank will work without any problems. https://radimrehurek.com/gensim/

For the topic modelling itself, I am going to use Gensim library by Radim Rehurek, which is very developer friendly and easy to use.