text
🚀 Exploring Topic Modeling with BERTopic 🤖
🚀 Exploring Topic Modeling with BERTopic 🤖
new_line
new_line
text
When you come across an interesting dataset, you often wonder:
When you come across an interesting dataset, you often wonder:
new_line
text
Which topics frequently appear in these documents? 🤔
Which topics frequently appear in these documents? 🤔
new_line
text
What is this data really about? 📊
What is this data really about? 📊
new_line
new_line
text
Topic modeling helps answer these questions by identifying recurring themes within a collection of documents. This process enables quick and efficient exploratory data analysis.
Topic modeling helps answer these questions by identifying recurring themes within a collection of documents. This process enables quick and efficient exploratory data analysis.
new_line
new_line
text
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. 🔗
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. 🔗
new_line
new_line
text
🔍 How do we make this work?
🔍 How do we make this work?
new_line
text
Here’s the stack we’re using:
Here’s the stack we’re using:
new_line
new_line
text
📂 Data Source ➡️ Hugging Face datasets with DuckDB for retrieval
📂 Data Source ➡️ Hugging Face datasets with DuckDB for retrieval
new_line
text
🧠 Text Embeddings ➡️ Sentence Transformers (all-MiniLM-L6-v2)
🧠 Text Embeddings ➡️ Sentence Transformers (all-MiniLM-L6-v2)
new_line
text
⚡ Dimensionality Reduction ➡️ RAPIDS cuML UMAP for GPU-accelerated performance
⚡ Dimensionality Reduction ➡️ RAPIDS cuML UMAP for GPU-accelerated performance
new_line
text
🔍 Clustering ➡️ RAPIDS cuML HDBSCAN for fast clustering
🔍 Clustering ➡️ RAPIDS cuML HDBSCAN for fast clustering
new_line
text
✂️ Tokenization ➡️ CountVectorizer
✂️ Tokenization ➡️ CountVectorizer
new_line
text
🔧 Representation Tuning ➡️ KeyBERTInspired + Hugging Face Inference Client with Meta-Llama-3-8B-Instruct
🔧 Representation Tuning ➡️ KeyBERTInspired + Hugging Face Inference Client with Meta-Llama-3-8B-Instruct
new_line
text
🌍 Visualization ➡️ Datamapplot library
🌍 Visualization ➡️ Datamapplot library
new_line
text
Check out the space and see how you can quickly generate topics from your dataset: 
Check out the space and see how you can quickly generate topics from your dataset: 
resource
https://huggingface.co/spaces/datasets-topics/topics-generator
space
datasets-topics/topics-generator
https://huggingface.co/spaces/datasets-topics/topics-generator
new_line
new_line
text
Powered by 
Powered by 
mention
@MaartenGr
MaartenGr
text
  - BERTopic 
  - BERTopic 
🚀 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 
👍
ijohn07 John6666 edison1 Chao2012 rennokki
❤️
Chao2012 korkakak MaartenGr
🔥
rennokki