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---
library_name: transformers
license: apache-2.0
datasets:
- aimagelab/ReT-M2KR
base_model:
- laion/CLIP-ViT-H-14-laion2B-s32B-b79K
pipeline_tag: visual-document-retrieval
---
# Model Card: ReT-2
Official implementation of ReT-2: Recurrence Meets Transformers for Universal Multimodal Retrieval.
This model features visual and textual backbones based on [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K).
<br>The backbones have been fine-tuned on the M2KR dataset.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/aimagelab/ReT-2
- **Paper:** [Recurrence Meets Transformers for Universal Multimodal Retrieval](https://arxiv.org/abs/2509.08897)
### Training Data
[aimagelab/ReT-M2KR](https://huggingface.co/datasets/aimagelab/ReT-M2KR)
## Citation
```
@article{caffagni2025recurrencemeetstransformers,
title={{Recurrence Meets Transformers for Universal Multimodal Retrieval}},
author={Davide Caffagni and Sara Sarto and Marcella Cornia and Lorenzo Baraldi and Rita Cucchiara},
journal={arXiv preprint arXiv:2509.08897},
year={2025}
}
``` |