Add pipeline tag and links to paper, code, and project page

#1
by nielsr HF Staff - opened
Files changed (1) hide show
  1. README.md +9 -4
README.md CHANGED
@@ -1,17 +1,20 @@
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  ---
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- license: apache-2.0
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  library_name: transformers
 
 
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  tags:
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  - vision-language-model
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  - reinforcement-learning
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  - grpo
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  - metaphor-understanding
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  - visual-reasoning
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- base_model: Qwen/Qwen2.5-VL
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  ---
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  # MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual RL
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  **MetaphorStar** is the first Multi-modal Large Language Model (MLLM) family trained via an **End-to-End Visual Reinforcement Learning (RL)** framework specifically designed to bridge the gap between literal perception ("seeing things as they are") and metaphorical understanding ("seeing things as we are").
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  Built upon the Qwen2.5-VL architecture, MetaphorStar achieves State-of-the-Art (SOTA) performance on image implication tasks and demonstrates robust generalization capabilities on complex visual reasoning benchmarks (e.g., MMMU, MathVerse).
@@ -65,7 +68,9 @@ messages = [
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  "role": "user",
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  "content": [
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  {"type": "image", "image": "path/to/metaphor_image.jpg"},
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- {"type": "text", "text": "True-false questions: The wilted plant in the office implies a stressful working environment.\n\nFirst, describe the image, then analyze the image implication, and finally reason to get the answer. Output the thinking process in <think></think> and the final correct answer in <answer></answer> tags."}
 
 
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  ]
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  }
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  ]
@@ -85,7 +90,7 @@ print(output_text)
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  @article{metaphorstar2026,
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  title={MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning},
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  author={Chenhao Zhang, Yazhe Niu, Hongsheng Li},
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- journal={Anonymous},
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  year={2026}
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  }
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  ```
 
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  ---
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+ base_model: Qwen/Qwen2.5-VL
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  library_name: transformers
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+ license: apache-2.0
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+ pipeline_tag: image-text-to-text
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  tags:
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  - vision-language-model
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  - reinforcement-learning
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  - grpo
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  - metaphor-understanding
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  - visual-reasoning
 
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  ---
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  # MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual RL
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+ [[Paper](https://huggingface.co/papers/2602.10575)] [[Project Page](https://metaphorstar.github.io)] [[GitHub](https://github.com/MING-ZCH/MetaphorStar)]
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+
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  **MetaphorStar** is the first Multi-modal Large Language Model (MLLM) family trained via an **End-to-End Visual Reinforcement Learning (RL)** framework specifically designed to bridge the gap between literal perception ("seeing things as they are") and metaphorical understanding ("seeing things as we are").
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  Built upon the Qwen2.5-VL architecture, MetaphorStar achieves State-of-the-Art (SOTA) performance on image implication tasks and demonstrates robust generalization capabilities on complex visual reasoning benchmarks (e.g., MMMU, MathVerse).
 
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  "role": "user",
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  "content": [
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  {"type": "image", "image": "path/to/metaphor_image.jpg"},
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+ {"type": "text", "text": "True-false questions: The wilted plant in the office implies a stressful working environment.
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+
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+ First, describe the image, then analyze the image implication, and finally reason to get the answer. Output the thinking process in <think></think> and the final correct answer in <answer></answer> tags."}
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  ]
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  }
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  ]
 
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  @article{metaphorstar2026,
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  title={MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning},
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  author={Chenhao Zhang, Yazhe Niu, Hongsheng Li},
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+ journal={arXiv preprint arXiv:2602.10575},
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  year={2026}
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  }
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  ```