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