Machine Learning

1 readers

1 users here now

Community Rules:

Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.

founded 1 year ago

MODERATORS

communick@academy.garden

[D] Package that Uses Embeddings to Generate Topics for Topic Modeling (alien.top)

submitted 11 months ago by NewerResearcher@alien.top to c/machinelearning@academy.garden

2 comments fedilink hide all child comments

I’ve used Bertopic and Top2Vec in Python but am wondering if there’s something similar in R that can use pre-trained models to generate topics? If not do you think investing time into building something like this would be useful to the community?

you are viewing a single comment's thread
view the rest of the comments

[–] NoIdeaAbaout@alien.top 1 points 11 months ago

There are much less alternatives, you could use keras in R but is actually a hassle. There are few alternatives for topic modelling in R:

the classical Latent Dirichlet allocation (LDA), you can use with a specific R library called: topicmodels, here you can find more information: https://www.tidytextmining.com/topicmodeling
You have also a package to better visualize topics, https://github.com/cpsievert/LDAvis
you can create something similar with BERT in R, I suggest to do something more simple: https://blogs.rstudio.com/ai/posts/2019-09-30-bert-r/
if you really want you can use python scripts in a r script