Add pipeline tag and improve documentation
Browse filesHi! I'm Niels from the community science team at Hugging Face.
I've opened this PR to improve the model card for Shesha:
- Added `pipeline_tag: other` to the metadata to better categorize the repository.
- Included a summary of the Shesha framework and the concept of geometric stability based on your paper "Geometric Stability: The Missing Axis of Representations".
- Added a citation section to help users correctly cite your research.
Please let me know if you have any questions!
README.md
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---
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tags:
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- arxiv:2601.09173
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- geometric-stability
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- steering
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- interpretability
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- computational-biology
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license: mit
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library_name: shesha-geometry
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---
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# Shesha: Geometric Stability Metric
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This is the official Hugging Face hub for the **Shesha** geometric stability metric, as presented in the paper
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## ๐ Quick Links
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* **๐ Paper:** [arXiv:2601.09173](https://arxiv.org/abs/2601.09173)
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## ๐ฆ Installation
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```bash
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pip install shesha-geometry
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---
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library_name: shesha-geometry
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license: mit
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pipeline_tag: other
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tags:
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- arxiv:2601.09173
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- geometric-stability
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- steering
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- interpretability
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- computational-biology
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---
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# Shesha: Geometric Stability Metric
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This is the official Hugging Face hub for the **Shesha** geometric stability metric, as presented in the paper [Geometric Stability: The Missing Axis of Representations](https://huggingface.co/papers/2601.09173).
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## Overview
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Analysis of learned representations typically focuses on *similarity*, measuring how closely embeddings align with external references. However, similarity reveals only what is represented, not whether that structure is robust.
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**Shesha** is a framework for measuring **geometric stability**, a distinct dimension that quantifies how reliably representational geometry holds under perturbation. Across 2,463 configurations in seven domains, research shows that stability and similarity are empirically uncorrelated ($\rho \approx 0.01$). This distinction makes Shesha a necessary complement to similarity for auditing representations across biological and computational systems.
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## ๐ Quick Links
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* **๐ Paper:** [arXiv:2601.09173](https://arxiv.org/abs/2601.09173)
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## ๐ฆ Installation
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```bash
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pip install shesha-geometry
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```
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## Key Applications
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Geometric stability provides actionable insights across multiple domains:
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- **Safety Monitoring:** Acts as a functional geometric canary to detect structural drift nearly 2$\times$ more sensitively than CKA.
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- **Controllability:** Supervised stability predicts linear steerability with high correlation ($\rho = 0.89$-$0.96$).
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- **Model Selection:** Dissociates from transferability, revealing the "geometric tax" that transfer optimization incurs.
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- **Scientific Analysis:** Predicts CRISPR perturbation coherence and neural-behavioral coupling.
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## Citation
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If you use Shesha or geometric stability in your research, please cite:
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```bibtex
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@article{raju2026geometric,
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title={Geometric Stability: The Missing Axis of Representations},
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author={Raju, Prashant C.},
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journal={arXiv preprint arXiv:2601.09173},
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year={2026}
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}
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```
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