nlphoseGUI VS gensim

Recently I completed work on a tool called nlphoseGUIBuilder that allows creation of complex NLP pipelines visually, without writing a single line of code ! It uses Blockly to enable creation of NLP pipelines using drag and drop.

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.