Maarten Grootendorst

MaartenGr

https://newsletter.maartengrootendorst.com

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reacted to asoria's post with ❤️ about 2 months ago

🚀 Exploring Topic Modeling with BERTopic 🤖 When you come across an interesting dataset, you often wonder: Which topics frequently appear in these documents? 🤔 What is this data really about? 📊 Topic modeling helps answer these questions by identifying recurring themes within a collection of documents. This process enables quick and efficient exploratory data analysis. I’ve been working on an app that leverages BERTopic, a flexible framework designed for topic modeling. Its modularity makes BERTopic powerful, allowing you to switch components with your preferred algorithms. It also supports handling large datasets efficiently by merging models using the BERTopic.merge_models approach. 🔗 🔍 How do we make this work? Here’s the stack we’re using: 📂 Data Source ➡️ Hugging Face datasets with DuckDB for retrieval 🧠 Text Embeddings ➡️ Sentence Transformers (all-MiniLM-L6-v2) ⚡ Dimensionality Reduction ➡️ RAPIDS cuML UMAP for GPU-accelerated performance 🔍 Clustering ➡️ RAPIDS cuML HDBSCAN for fast clustering ✂️ Tokenization ➡️ CountVectorizer 🔧 Representation Tuning ➡️ KeyBERTInspired + Hugging Face Inference Client with Meta-Llama-3-8B-Instruct 🌍 Visualization ➡️ Datamapplot library Check out the space and see how you can quickly generate topics from your dataset: https://huggingface.co/spaces/datasets-topics/topics-generator Powered by @MaartenGr - BERTopic

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Introducing BERTopic Integration with Hugging Face Hub

May 31, 2023

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reacted to asoria's post with ❤️ about 2 months ago

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🚀 Exploring Topic Modeling with BERTopic 🤖

When you come across an interesting dataset, you often wonder:
Which topics frequently appear in these documents? 🤔
What is this data really about? 📊

Topic modeling helps answer these questions by identifying recurring themes within a collection of documents. This process enables quick and efficient exploratory data analysis.

I’ve been working on an app that leverages BERTopic, a flexible framework designed for topic modeling. Its modularity makes BERTopic powerful, allowing you to switch components with your preferred algorithms. It also supports handling large datasets efficiently by merging models using the BERTopic.merge_models approach. 🔗

🔍 How do we make this work?
Here’s the stack we’re using:

📂 Data Source ➡️ Hugging Face datasets with DuckDB for retrieval
🧠 Text Embeddings ➡️ Sentence Transformers (all-MiniLM-L6-v2)
⚡ Dimensionality Reduction ➡️ RAPIDS cuML UMAP for GPU-accelerated performance
🔍 Clustering ➡️ RAPIDS cuML HDBSCAN for fast clustering
✂️ Tokenization ➡️ CountVectorizer
🔧 Representation Tuning ➡️ KeyBERTInspired + Hugging Face Inference Client with Meta-Llama-3-8B-Instruct
🌍 Visualization ➡️ Datamapplot library
Check out the space and see how you can quickly generate topics from your dataset: datasets-topics/topics-generator

Powered by @MaartenGr - BERTopic

replied to severo's post 5 months ago

Hi! Thank you for reaching out. I generally like to keep the post either on my newsletter or Medium where I have both gained some followers.

Having said that, I would be open to a collaboration with HF to publish it. Due to the time spent on this guide, it would need to be more than just publishing it as a community blog.

New activity in MaartenGr/BERTopic_Wikipedia 9 months ago