BERTopic
OCTIS
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| BERTopic | OCTIS | |
|---|---|---|
| 22 | 7 | |
| 5,488 | 681 | |
| - | 1.9% | |
| 8.2 | 6.0 | |
| 11 days ago | 4 months ago | |
| Python | Python | |
| MIT License | MIT License |
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BERTopic
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SBERT Embeddings from Conversations
Consider also BERTTopic which is built on sentence transformers and can be used to cluster documents.
Try out this notebook which comes with the BERTopic repository.
- How can I group domain specific keywords based on their word embeddings?
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Introducing the Semantic Graph
A number of excellent topic modeling libraries exist in Python today. BERTopic and Top2Vec are two of the most popular. Both use sentence-transformers to encode data into vectors, UMAP for dimensionality reduction and HDBSCAN to cluster nodes.
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[D] How to best extract product benefits/problems from customer reviews using NLP?
I have experimented a bit with BERTopic but didn't find the results very useful. The issue is, that it is very important what exactly people are liking or disliking about the products, not just the fact that they are talking about specific aspects.
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Using Transformer for Topic Modeling - what are the options?
What are the state-of-the-art libraries/models out there? So far I've looked into BERTopic (https://github.com/MaartenGr/BERTopic).
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Latest trends in topic modelling?
BERT actually does pretty decent!
OCTIS
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(NLP) Best practices for topic modeling and generating interesting topics?
My team and I have recently released a python library called OCTIS (https://github.com/mind-Lab/octis) that allows you to automatically optimize the hyperparameters of a topic model according to a given evaluation metric (not log-likelihood). I guess, in your case, you might be interested in topic coherence. So you will get good quality topics with a low effort on the choice of the hyperparameters. Also, we included some state-of-the-art topic models, e.g. contextualized topic models (https://github.com/MilaNLProc/contextualized-topic-models).
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Latest trends in topic modelling?
Silvia Terragni (a coauthor on the above) also brought a topic modelling library OCTIS which was exhibited as a demo paper and aims to be the huggingface transformers of topic modelling - it includes wrappers around the above model as well as and LDA and some baselines as well as some tools and frameworks for comparing them.
What are some alternatives?
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
gensim - Topic Modelling for Humans
GuidedLDA - semi supervised guided topic model with custom guidedLDA
contextualized-topic-models - A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
PyABSA - Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
scattertext - Beautiful visualizations of how language differs among document types.
clip-as-service - 🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
beto - BETO - Spanish version of the BERT model
zeroshot_topics - Topic Inference with Zeroshot models
auto-sklearn - Automated Machine Learning with scikit-learn
smaller-labse - Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE