BERTopic
Leveraging BERT and c-TF-IDF to create easily interpretable topics. (by MaartenGr)
umap
Uniform Manifold Approximation and Projection (by lmcinnes)
| BERTopic | umap | |
|---|---|---|
| 22 | 10 | |
| 5,564 | 6,946 | |
| - | - | |
| 8.2 | 8.3 | |
| 3 days ago | 9 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BERTopic
Posts with mentions or reviews of BERTopic.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-03.
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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
umap
Posts with mentions or reviews of umap.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-29.
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[OC] Clustering Images with OpenAI CLIP, T-SNE, UMAP & Plotly
UMAP GitHub repository: https://github.com/lmcinnes/umap
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UMAP clustering in Ruby
Uniform Manifold Approximation and Projection (UMAP) is a well-known dimensionality reduction method along with t-SNE.
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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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Using the 80:20 rule, what top 20% of your tools, statistical tests, activities, etc. do you use to generate 80% of your results?
As with anything, it depends on the problem. But T-SNE and UMAP are often good.
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[D] In UMAP and PyNNDescent, the conversion of Cosine and Correlation measures to distance metric seems problematic
UMAP distances.py: umap/distances.py at master · lmcinnes/umap (github.com)
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I built an Image Search Engine using OpenAI CLIP and Images from Wikimedia
I used for this project Flask and OpenAI CLIP. For the vector search I used approximate nearest neighbors provided by spotify/annoy. I used Flask-SQLAlchemy with GeoAlchemy2 to query GPS coordinates. The embedding was done using UMAP.
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We Analyzed 425,909 Favicons
side note: instead of t-SNE consider UMAP - provides better results (and it's much faster) https://github.com/lmcinnes/umap
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Finding correlating features in a large dataset.
Sounds like a job for UMAP https://github.com/lmcinnes/umap ?
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The most perplexing bug I've ever seen
I am a fairly experienced python developer/researcher (about 10 years), and have found a bug that breaks all of my intuitions. I am messing with the [UMAP](https://github.com/lmcinnes/umap) repository and trying to add the option to disable some additional features. I've stripped everything from it but have a [quick test that will run my UMAP version and compare the outputs with what the original gave](https://github.com/Andrew-Draganov/probabilistic_dim_reduction/blob/master/umap/nndescent_umap_test.py). Managing my random seeds, same inputs, all that.
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Question about numpy method I found in github project
I'm currently reading through a project on github, https://github.com/lmcinnes/umap, and in `umap/umap_.py` at line 2287, they have this:
What are some alternatives?
When comparing BERTopic and umap you can also consider the following projects:
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
minisom - :red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
gensim - Topic Modelling for Humans
giotto-tda - A high-performance topological machine learning toolbox in Python
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
GuidedLDA - semi supervised guided topic model with custom guidedLDA
Traccar - Traccar GPS Tracking System
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.
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
PyABSA - Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
Openstreetmap - The Rails application that powers OpenStreetMap