https://github.com/radames/sentence-embeddings-visualization

https://github.com/radames/sentence-embeddings-visualization

embeddings huggingface observablehq transformers visualization

Last synced: 11 days ago
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• Host: GitHub
• URL: https://github.com/radames/sentence-embeddings-visualization
• Owner: radames
• Created: 2022-02-23T15:17:54.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-02-23T15:31:25.000Z (over 2 years ago)
• Last Synced: 2024-10-26T22:30:49.837Z (25 days ago)
• Topics: embeddings, huggingface, observablehq, transformers, visualization
• Language: Python
• Homepage: https://huggingface.co/spaces/radames/sentence-embeddings-visualization
• Size: 78.1 KB
• Stars: 8
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
---

title: Sentence Embeddings Visualization

emoji: 📈

colorFrom: green

colorTo: indigo

sdk: gradio

app_file: app.py

pinned: false

---

# Hugging Face Spaces + Observable

### Sentence Embeddings Visualization

Recently I've been exploring [Hugging face Spaces](https://huggingface.co/spaces) and [sentence-transformers](https://huggingface.co/sentence-transformers) to build an application to generate text embeddings and clustering visualization.

Currently, the quickest way to build interactive ML apps with Python (backend/frontend), afaik, is to use [Streamlit](https://streamlit.io/) or [Gradio](https://www.gradio.app/). To embed an Observable notebook on Streamlit, you can use this custom component [streamlit-observable](https://github.com/asg017/streamlit-observable) 

This [Observable notebook](https://observablehq.com/@radames/hugging-face-spaces-observable-sentence-embeddings) is the frontend application for this [Hugging Face Spaces](https://huggingface.co/spaces/radames/sentence-embeddings-visualization) app.

This notebook explores another way to integrate Observable inside Hugging Face Spaces. Currently,  [HF Spaces supports](https://huggingface.co/docs/hub/spaces#streamlit-and-gradio) Streamlit and Gradio or a simple static web page. 

The concept here is to use this entire notebook as the frontend and data visualization application for the [ML Flask/Python](https://huggingface.co/spaces/radames/sentence-embeddings-visualization/blob/main/app.py#L37-L75) backend.

* The index route renders a [simple HTML template](https://huggingface.co/spaces/radames/sentence-embeddings-visualization/blob/main/templates/index.html) containing [Observable Runtime API code](https://observablehq.com/@observablehq/downloading-and-embedding-notebooks).

* A single function, triggered by a POST request to \`run-umap\`, returns a low dimensional representation of the original sentence transformers embeddings using UMAP and cluster analysis with HDBSCAN.

* All the visualization and interactive magic happen on the Javascript code inside the Observable Notebook.