Top2Vec
Top2Vec learns jointly embedded topic, document and word vectors. (by ddangelov)
umap
Uniform Manifold Approximation and Projection (by lmcinnes)
| Top2Vec | umap | |
|---|---|---|
| 13 | 10 | |
| 2,843 | 6,946 | |
| - | - | |
| 7.0 | 8.3 | |
| 5 months ago | 8 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" 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.
Top2Vec
Posts with mentions or reviews of Top2Vec.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-30.
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[D] Is it better to create a different set of Doc2Vec embeddings for each group in my dataset, rather than generating embeddings for the entire dataset?
I'm using Top2Vec with Doc2Vec embeddings to find topics in a dataset of ~4000 social media posts. This dataset has three groups:
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Tips for best Top2Vec (HDBSCAN) usage
I asked in a previous post for advice about how to find insight in unstructured text data. Almost everyone recommended BERTopic, but I wasn't able to run BERTopic on my machine locally (segmentation fault). Fortunately, I found Top2Vec, which uses HBDSCAN and UMAP to quickly find good topics in uncleaned(!) text data.
- 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.
- Top2Vec: Embed topics, documents and word vectors
- How to cluster articles about software vulnerabilities?
- Ciencia de Dados - Classificacao de texto
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Extracting topics from 250k facebook posts
Since you already have the facebook posts, you can use top2vec https://github.com/ddangelov/Top2Vec
- [D] Good algorithm for clustering big data (sentences represented as embeddings)?
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SOTA for Topic Modeling
Here's an implementation that uses UMAP and HDBSCAN: https://github.com/ddangelov/Top2Vec but you could use a semi-supervised algorithm in the clustering step if you wanted specific topics.
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 Top2Vec and umap you can also consider the following projects:
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
minisom - :red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
giotto-tda - A high-performance topological machine learning toolbox in Python
faiss - A library for efficient similarity search and clustering of dense vectors.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Milvus - A cloud-native vector database, storage for next generation AI applications
Traccar - Traccar GPS Tracking System
hdbscan - A high performance implementation of HDBSCAN clustering.
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 🚀
GuidedLDA - semi supervised guided topic model with custom guidedLDA
Openstreetmap - The Rails application that powers OpenStreetMap