Add GitHub link, paper metadata, and improve model card

#1
by nielsr HF Staff - opened
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  1. README.md +18 -12
README.md CHANGED
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  ---
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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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  - video
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  - retrieval
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  - reranking
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  - qwen3-vl
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- base_model: Qwen/Qwen3-VL-8B-Instruct
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- pipeline_tag: video-text-to-text
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  ---
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  # RankVideo
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- Video-native reasoning reranker for text-to-video retrieval. Fine-tuned from Qwen3-VL-8B-Instruct.
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- ## Reference
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- - [arXiv:2602.02444](https://arxiv.org/abs/2602.02444)
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- ## Training Data
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-
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- This model was trained using the [MultiVENT 2.0 dataset](https://huggingface.co/datasets/hltcoe/MultiVENT2.0 ).
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  ## Usage
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- ```
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  from rankvideo import VLMReranker
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  reranker = VLMReranker(model_path="hltcoe/RankVideo")
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  scores = reranker.score_batch(
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  queries=["person playing guitar"],
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  video_paths=["/path/to/video.mp4"],
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  )
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  print(f"Relevance score: {scores[0]['logit_delta_yes_minus_no']:.3f}")
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-
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  ```
 
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  ## BibTeX
 
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  ```bibtex
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  @misc{skow2026rankvideoreasoningrerankingtexttovideo,
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  title={RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval},
@@ -50,4 +55,5 @@ print(f"Relevance score: {scores[0]['logit_delta_yes_minus_no']:.3f}")
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  archivePrefix={arXiv},
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  primaryClass={cs.IR},
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  url={https://arxiv.org/abs/2602.02444},
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- }
 
 
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  ---
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+ base_model: Qwen/Qwen3-VL-8B-Instruct
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  language:
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  - en
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+ license: mit
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+ pipeline_tag: video-text-to-text
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+ library_name: transformers
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+ arxiv: 2602.02444
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  tags:
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  - video
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  - retrieval
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  - reranking
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  - qwen3-vl
 
 
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  ---
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  # RankVideo
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+ RankVideo is a video-native reasoning reranker for text-to-video retrieval, fine-tuned from [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct).
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+ The model explicitly reasons over query-video pairs using video content to assess relevance. It was introduced in the paper [RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval](https://huggingface.co/papers/2602.02444).
 
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+ - **Repository:** [https://github.com/tskow99/RANKVIDEO-Reasoning-Reranker](https://github.com/tskow99/RANKVIDEO-Reasoning-Reranker)
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+ - **Paper:** [RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval](https://arxiv.org/abs/2602.02444)
 
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+ ## Training Data
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+ This model was trained using the [MultiVENT 2.0 dataset](https://huggingface.co/datasets/hltcoe/MultiVENT2.0).
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  ## Usage
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+ You can use the model for scoring query-video pairs via the `rankvideo` library as follows:
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+ ```python
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  from rankvideo import VLMReranker
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  reranker = VLMReranker(model_path="hltcoe/RankVideo")
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+ # Score query-video pairs for relevance
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  scores = reranker.score_batch(
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  queries=["person playing guitar"],
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  video_paths=["/path/to/video.mp4"],
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  )
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  print(f"Relevance score: {scores[0]['logit_delta_yes_minus_no']:.3f}")
 
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  ```
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+
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  ## BibTeX
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+
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  ```bibtex
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  @misc{skow2026rankvideoreasoningrerankingtexttovideo,
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  title={RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval},
 
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  archivePrefix={arXiv},
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  primaryClass={cs.IR},
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  url={https://arxiv.org/abs/2602.02444},
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+ }
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+ ```