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Improve CheXpert-plus-RRG dataset card: Add metadata and links

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This PR significantly enhances the CheXpert-plus-RRG dataset card by making the following improvements:

- Adds `task_categories: ['image-text-to-text']` to the YAML metadata, improving discoverability for relevant tasks.
- Specifies the `license: mit` in the YAML metadata, aligning with the license declared in the associated GitHub repository.
- Includes `language: en`, `medical`, `radiology`, `mllm`, `hallucination`, `report-generation`, and `chest-x-ray` as `tags` to provide more specific context and improve searchability.
- Adds explicit links to the paper, project page, and GitHub repository at the top of the content for easier access to related resources.

These changes make the dataset card more informative and user-friendly for the Hugging Face community.

Files changed (1) hide show
  1. README.md +20 -2
README.md CHANGED
@@ -37,16 +37,34 @@ configs:
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  data_files:
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  - split: valid
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  path: impression_section/valid-*
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
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  # CheXpert-plus-RRG: Radiology Report Generation Subsets
 
 
 
 
 
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  CheXpert-plus-RRG curates two report-generation benchmarks from the [CheXpert Plus validation split](https://stanfordmlgroup.github.io/competitions/chexpert/). Each configuration links a frontal chest radiograph with its matching clinical narrative.
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  ---
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  ## 📚 Dataset Overview
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  | Subset | Section Target | Split | #Samples | View Coverage |
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- |----------------------|----------------|-------|----------|----------------------|
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- | `findings_section` | Findings | valid | 62 | Frontal (AP/PA) |
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  | `impression_section` | Impression | valid | 202 | Frontal (AP/PA) |
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  - Packaged as a validation-only suite for **benchmarking and zero-shot evaluation**.
 
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  data_files:
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  - split: valid
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  path: impression_section/valid-*
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+ task_categories:
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+ - image-text-to-text
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+ license: mit
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+ language:
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+ - en
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+ tags:
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+ - medical
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+ - radiology
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+ - mllm
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+ - hallucination
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+ - report-generation
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+ - chest-x-ray
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  ---
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+
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  # CheXpert-plus-RRG: Radiology Report Generation Subsets
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+
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+ Paper: [CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding](https://huggingface.co/papers/2509.23379)
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+ Project Page: [https://x-izhang.github.io/CCD/](https://x-izhang.github.io/CCD/)
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+ Code: [https://github.com/X-iZhang/CCD](https://github.com/X-iZhang/CCD)
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+
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  CheXpert-plus-RRG curates two report-generation benchmarks from the [CheXpert Plus validation split](https://stanfordmlgroup.github.io/competitions/chexpert/). Each configuration links a frontal chest radiograph with its matching clinical narrative.
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  ---
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  ## 📚 Dataset Overview
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  | Subset | Section Target | Split | #Samples | View Coverage |
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+ |----------------------|----------------|-------|----------|----------------------|\
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+ | `findings_section` | Findings | valid | 62 | Frontal (AP/PA) |\
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  | `impression_section` | Impression | valid | 202 | Frontal (AP/PA) |
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  - Packaged as a validation-only suite for **benchmarking and zero-shot evaluation**.