Add model card and metadata

Hi! I'm Niels from the Hugging Face community team. I noticed this repository is currently a placeholder.

This PR adds a proper model card, linking it to the research paper [Comprehensive language-image pre-training for 3D medical image understanding](https://huggingface.co/papers/2510.15042), and includes the relevant `pipeline_tag` metadata to improve discoverability on the Hub.

README.md

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 license: mit
+pipeline_tag: image-text-to-text
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-This repository will be populated very soon.
+# COLIPRI: Comprehensive Language-Image Pre-training for 3D Medical Image Understanding
+
+COLIPRI (Comprehensive Language-Image Pre-training) is a family of vision-language encoders designed for 3D medical image understanding. 
+
+The model was introduced in the paper: [Comprehensive language-image pre-training for 3D medical image understanding](https://huggingface.co/papers/2510.15042).
+
+## Description
+
+COLIPRI aligns 3D medical images with paired text using a multi-objective pre-training strategy. It combines vision-language alignment with a report generation objective and vision-only pre-training, allowing the model to benefit from both image-only and paired image-text 3D datasets.
+
+The COLIPRI encoders achieve state-of-the-art performance across various tasks:
+- **Radiological Report Generation**: Generating text descriptions from 3D scans.
+- **Semantic Segmentation**: Downstream adaptation for anatomical and pathological segmentation.
+- **Zero-shot Classification**: Predicting labels without task-specific fine-tuning.
+- **Classification Probing**: Providing strong feature representations for medical diagnostics.
+
+## Citation
+
+```bibtex
+@article{wald2025comprehensive,
+  title={Comprehensive language-image pre-training for 3D medical image understanding},
+  author={Wald, Tassilo and Hamamci, Ibrahim Ethem and Gao, Yuan and Bond-Taylor, Sam and Sharma, Harshita and Ilse, Maximilian and Lo, Cynthia and Melnichenko, Olesya and Codella, Noel C. F. and Wetscherek, Maria Teodora and others},
+  journal={arXiv preprint arXiv:2510.15042},
+  year={2025}
+}
+```