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Remove arXiv ID from metadata and add pipeline tag

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This PR improves the model card by:
- Removing the `arxiv:2609.33437` tag from the YAML metadata, since arXiv IDs should only be referenced in the Markdown content, not in metadata.
- Adding the correct `pipeline_tag: image-classification` so the model is discoverable under the appropriate Hugging Face category.

No other content changes are needed.

Files changed (1) hide show
  1. README.md +4 -3
README.md CHANGED
@@ -1,16 +1,17 @@
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  ---
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- license: mit
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  base_model: openai/clip-vit-base-patch32
 
 
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  tags:
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  - smat
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  - model-merging
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  - vision
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- - arxiv:2609.33437
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  ---
 
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  # CLIP ViT base-patch32 路 FT 路 RESISC45
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  A FT expert from Table 2 of **SMAT: Simple and Efficient Merge-Aware Training** (seed 42). SMAT trains experts with model merging in mind.
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  [Paper](https://arxiv.org/abs/2609.33437) 路 [GitHub & usage](https://github.com/egangu/smat/blob/main/docs/HUGGINGFACE.md)
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- `encoder.pt` is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
 
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  ---
 
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  base_model: openai/clip-vit-base-patch32
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+ license: mit
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+ pipeline_tag: image-classification
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  tags:
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  - smat
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  - model-merging
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  - vision
 
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  ---
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+
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  # CLIP ViT base-patch32 路 FT 路 RESISC45
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  A FT expert from Table 2 of **SMAT: Simple and Efficient Merge-Aware Training** (seed 42). SMAT trains experts with model merging in mind.
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  [Paper](https://arxiv.org/abs/2609.33437) 路 [GitHub & usage](https://github.com/egangu/smat/blob/main/docs/HUGGINGFACE.md)
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+ `encoder.pt` is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.