SimCSE
BERTopic
| SimCSE | BERTopic | |
|---|---|---|
| 2 | 22 | |
| 3,255 | 5,600 | |
| 1.3% | - | |
| 0.0 | 8.2 | |
| 8 months ago | 1 day ago | |
| Python | Python | |
| MIT License | MIT License |
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SimCSE
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BERT-Based Clustering on a Corpus of Genre Samples Kinda Sucks. Why?
Base BERT sentence embeddings are just not good for a couple of reasons and there's some research papers that show this. You can try SimCSE, Google's USE or SBERT as mentioned previously and you'll get better output. It's just an inherent flaw to base BERT that it can't produce good sentence embeddings. Papers have shown you probably will get better scores using GloVe embeddings from scratch than base BERT.
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State of the Art in Sentence Embeddings
To answer your question about sentence embedding SOTA, it is not s-Bert and hasn't been for a while. SimCSE officially takes the crown since it's been presented at a conference, though according to paperswithcode's benchmark leaderboard there are other papers on arxiv that report higher performance on STS and similar tasks such as DCPCSE. Having tried both of these for my use case I found SimCSE to be better but YMMV.
BERTopic
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how can a top2vec output be improved
Try experimenting with different hyperparameters, clustering algorithms and embedding representations. Try https://github.com/MaartenGr/BERTopic/tree/master/bertopic
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SBERT Embeddings from Conversations
Try out this notebook which comes with the BERTopic repository.
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Sentence transformers (BERTopic) on a Macbook Air
After some googling, I found this (but for M1 chip Mac) --I wonder if I'm stuck. Is this laptop just not up for the job of working with sentence transformers? Appreciate your advice
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Comparing BERTopic to human raters
Most has already been said and I am not sure how relevant this is but since you are focusing on human raters it might be worthwhile to mention that there is a Pull Request in BERTopic that allows you to use models on top of the default pipeline that further fine-tunes the topic representation. In theory, this would allow you to even use ChatGPT or any of the other OpenAI models to label the topics. From a human annotator perspective, this might be interesting to pursue.
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text clustering with XLNET, ROBERTA, ELMO and other pretrained models
The BERTopic library allows you to plug and play any type of embedding.
- 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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Classifying unstructured text: sentences, phrases, lists of words
BERTopic is a library to consider if you want something that groups data by topic.
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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.
- Classify texts using known categories, NLP
What are some alternatives?
PromCSE - Code for "Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (EMNLP 2022)"
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
inltk - Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need
gensim - Topic Modelling for Humans
AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
DiffCSE - Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"
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