Overview
This exploratory application finds groups and relationships in unstructured text using sentence-level embeddings and interactive visualization.
The problem
Keyword-based grouping can miss documents that use different words to express closely related ideas. Reviewing those relationships manually becomes difficult as the collection grows.
The solution
The application creates Sentence-BERT embeddings, clusters the resulting vectors with scikit-learn, and exposes the output through a Streamlit interface for exploration.
System architecture
The workflow converts text into semantic vectors before clustering and presenting the resulting groups for inspection.
- 01Text input
- 02Sentence-BERT encoding
- 03Embedding vectors
- 04Clustering
- 05Interactive visualization
Key capabilities
Semantic representation
Represents sentences as dense vectors so conceptually related text can be compared beyond exact keyword overlap.
Configurable clustering
Uses scikit-learn to group embedding vectors for exploratory analysis.
Interactive exploration
Presents clustering output through Streamlit so results can be inspected without a separate analysis environment.
Engineering considerations
Model selection
The selected sentence-transformer model affects both representation quality and inference cost. The appropriate balance depends on the language and shape of the source text.
Cluster interpretation
Clustering produces candidate structure rather than an objective taxonomy. Results still require domain-aware review.
Technology stack
- Modeling: Sentence-BERT, Hugging Face, scikit-learn
- Application: Python, Streamlit
Outcome
The project provides a working environment for exploring semantic relationships in text and comparing how embedding and clustering choices affect the resulting groups.