Add GitHub link, paper metadata, and improve model card
#1
by
nielsr
HF Staff
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README.md
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---
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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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- [arXiv:2602.02444](https://arxiv.org/abs/2602.02444)
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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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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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## 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},
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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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## 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},
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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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