Improve model card: Add pipeline tag, library name, code link, and update image paths
Browse filesThis PR enhances the model card for `CapRL-Eval-3B` by:
- Adding `pipeline_tag: image-text-to-text` to improve discoverability on the Hub.
- Specifying `library_name: transformers` as the model is compatible with the π€ Transformers library, enabling an automated "How to use" widget.
- Consolidating the paper links and explicitly adding a link to the GitHub repository for easy access to the code.
- Updating relative image paths to absolute Hugging Face Hub paths for improved rendering robustness.
Please review and merge this PR if everything looks good.
README.md
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license: apache-2.0
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---
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**CapRL-Eval-3B** is the model used for answering questions based on captions, and it is a finetuned version of Qwen2.5-VL-3B. When dealing with tasks such as ChartQA (not multiple-choice questions), it provides more stable output formatting.
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filtered 75K QA dataset as the training set, we obtained a highly capable captioner, CapRL-3B.
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<p align="center">
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<img src="./assets/teaser.png" alt="Main Results on GPT2" width="750"/>
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</p>
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<p align="center">
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<img src="./assets/performance.png" alt="Main Results on GPT2" width="750"/>
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</p>
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## Key Features
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## Cases
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<p align="center">
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<img src="./assets/comparison.png" alt="Main Results on GPT2" width="750"/>
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</p>
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<p align="center">
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<img src="./assets/info_caprl.png" alt="Main Results on GPT2" width="750"/>
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<img src="./assets/info_caprl2.png" alt="Main Results on GPT2" width="750"/>
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<p align="center">
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<img src="./assets/natural_caprl.png" alt="Main Results on GPT2" width="750"/>
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</p>
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---
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license: apache-2.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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# CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning
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π<a href="https://huggingface.co/papers/2509.22647">Paper</a> | π»<a href="https://github.com/InternLM/CapRL">Code</a> | π€<a href="https://huggingface.co/internlm/CapRL-3B">CapRL-3B Model</a> |
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π€<a href="https://huggingface.co/datasets/internlm/CapRL-2M">CapRL-2M Dataset</a> |π€<a href="https://huggingface.co/collections/long-xing1/caprl-68d64ac32ded31596c36e189">CapRL Collection</a>
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**CapRL-Eval-3B** is the model used for answering questions based on captions, and it is a finetuned version of Qwen2.5-VL-3B. When dealing with tasks such as ChartQA (not multiple-choice questions), it provides more stable output formatting.
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filtered 75K QA dataset as the training set, we obtained a highly capable captioner, CapRL-3B.
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<p align="center">
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<img src="https://huggingface.co/internlm/CapRL-Eval-3B/resolve/main/assets/teaser.png" alt="Main Results on GPT2" width="750"/>
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</p>
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<p align="center">
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<img src="https://huggingface.co/internlm/CapRL-Eval-3B/resolve/main/assets/performance.png" alt="Main Results on GPT2" width="750"/>
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</p>
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## Key Features
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## Cases
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<p align="center">
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<img src="https://huggingface.co/internlm/CapRL-Eval-3B/resolve/main/assets/comparison.png" alt="Main Results on GPT2" width="750"/>
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</p>
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<p align="center">
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<img src="https://huggingface.co/internlm/CapRL-Eval-3B/resolve/main/assets/info_caprl.png" alt="Main Results on GPT2" width="750"/>
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</p>
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<p align="center">
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<img src="https://huggingface.co/internlm/CapRL-Eval-3B/resolve/main/assets/info_caprl2.png" alt="Main Results on GPT2" width="750"/>
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</p>
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<p align="center">
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<img src="https://huggingface.co/internlm/CapRL-Eval-3B/resolve/main/assets/natural_caprl.png" alt="Main Results on GPT2" width="750"/>
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</p>
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