Add pipeline_tag and library_name metadata
Browse filesThis PR enhances the model card by adding the `pipeline_tag` as `feature-extraction` and `library_name` as `dictionary-learning` to the YAML metadata.
- The `pipeline_tag: feature-extraction` helps users discover the model more easily through filtering by task on the Hugging Face Hub, as these Sparse Autoencoders are designed to extract interpretable features from model activations.
- The `library_name: dictionary-learning` clearly indicates the primary library used for these SAEs, aiding in setup and usage.
README.md
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license: mit
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language:
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- en
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---
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# Model Card for MAIRA-2-SAE
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This is a collection of sparse autoencoders (SAEs) trained on the residual stream of layer 15 of [MAIRA-2](https://huggingface.co/microsoft/maira-2), and described in the preprint ['Insights into a radiology-specialised multimodal large language model with sparse autoencoders'](https://arxiv.org/abs/2507.12950), presented at the [Actionable Interpretability Workshop @ ICML 2025](https://actionable-interpretability.github.io/).
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---
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language:
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- en
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license: mit
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pipeline_tag: feature-extraction
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library_name: dictionary-learning
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---
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# Model Card for MAIRA-2-SAE
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This is a collection of sparse autoencoders (SAEs) trained on the residual stream of layer 15 of [MAIRA-2](https://huggingface.co/microsoft/maira-2), and described in the preprint ['Insights into a radiology-specialised multimodal large language model with sparse autoencoders'](https://arxiv.org/abs/2507.12950), presented at the [Actionable Interpretability Workshop @ ICML 2025](https://actionable-interpretability.github.io/).
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