Embedding Clusterer
A semantic clustering library for grouping LLM responses using embedding-based methods with KMedoids and Davies-Bouldin Index for cluster evaluation.
Installation
pip install embedding-clusterer
Features
- Embedding-based Clustering: Use sentence embeddings to group similar responses
- KMedoids Algorithm: Robust clustering with actual representative samples
- Davies-Bouldin Index: Automatic cluster evaluation and quality assessment
- Transformer Models: Compatible with Hugging Face sentence-transformers
Usage
from embedding_clusterer import SemanticClusterer
clusterer = SemanticClusterer()
clusters = clusterer.cluster(texts, num_clusters=3)
Dependencies
- numpy
- scikit-learn
- sentence-transformers
License
See LICENSE file for details.
1 maintainer
Release files for embedding-clusterer 0.1.0
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Total release size: 9.5 kB
Release files / embedding_clusterer-0.1.0.tar.gz
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