Add sample usage section to model card

README.md

@@ -1,11 +1,11 @@
 ---
-library_name: transformers
-license: apache-2.0
-datasets:
-- aimagelab/ReT-M2KR
 base_model:
 - google/siglip2-large-patch16-256
 - colbert-ir/colbertv2.0
+datasets:
+- aimagelab/ReT-M2KR
+library_name: transformers
+license: apache-2.0
 pipeline_tag: visual-document-retrieval
 ---
 
@@ -13,8 +13,8 @@ pipeline_tag: visual-document-retrieval
 
 Official implementation of ReT-2: Recurrence Meets Transformers for Universal Multimodal Retrieval.
 
-This model features a visual backbone based on [google/siglip2-large-patch16-256](https://huggingface.co/google/siglip2-large-patch16-256) and a textual backbone based on [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0). 
-<br>The backbones have been fine-tuned on the M2KR dataset. 
+This model features a visual backbone based on [google/siglip2-large-patch16-256](https://huggingface.co/google/siglip2-large-patch16-256) and a textual backbone based on [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0).
+<br>The backbones have been fine-tuned on the M2KR dataset.
 
 
 ### Model Sources
@@ -28,11 +28,65 @@ This model features a visual backbone based on [google/siglip2-large-patch16-256
 ### Training Data
 [aimagelab/ReT-M2KR](https://huggingface.co/datasets/aimagelab/ReT-M2KR)
 
+## Use with 🤗's Transformers
+
+```python
+from src.models import Ret2Model
+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+import torch.nn.functional as F
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+headers = {
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+}
+
+query_img_url = 'https://upload.wikimedia.org/wikipedia/commons/8/84/Ghirlandina_%28Modena%29.jpg'
+response = requests.get(query_img_url, headers=headers)
+query_image = Image.open(BytesIO(response.content)).convert('RGB')
+query_text = 'Where is this building located?'
+
+passage_img_url = 'https://upload.wikimedia.org/wikipedia/commons/0/09/Absidi_e_Ghirlandina.jpg'
+response = requests.get(query_img_url, headers=headers)
+passage_image = Image.open(BytesIO(response.content)).convert('RGB')
+passage_text = (
+    "The Ghirlandina is the bell tower of the Cathedral of Modena, in Modena, Italy. "
+    "It is 86.12 metres (282.7 ft) high and is the symbol of the city. "
+    "It was built in Romanesque style in the 12th century and is part of a UNESCO World Heritage Site."
+)
+
+model = Ret2Model.from_pretrained('aimagelab/ReT2-M2KR-ColBERT-SigLIP2-ViT-L', device_map=device)
+
+query_txt_inputs = model.tokenizer([query_text], return_tensors='pt').to(device)
+query_img_inputs = model.image_processor([query_image], return_tensors='pt').to(device)
+passage_txt_inputs = model.tokenizer([passage_text], return_tensors='pt').to(device)
+passage_img_inputs = model.image_processor([passage_image], return_tensors='pt').to(device)
+
+with torch.inference_mode():
+    query_feats = model.get_ret_features(
+        input_ids=query_txt_inputs.input_ids,
+        attention_mask=query_txt_inputs.attention_mask,
+        pixel_values=query_img_inputs.pixel_values
+    )
+
+    passage_feats = model.get_ret_features(
+        input_ids=passage_txt_inputs.input_ids,
+        attention_mask=passage_txt_inputs.attention_mask,
+        pixel_values=passage_img_inputs.pixel_values
+    )
+
+    sim = F.normalize(query_feats, p=2, dim=-1) @ F.normalize(passage_feats, p=2, dim=-1).T
+
+print(f"query-passage similarity: {sim.item():.3f}")
+```
 
 ## Citation
 ```
 @article{caffagni2025recurrencemeetstransformers,
-      title={{Recurrence Meets Transformers for Universal Multimodal Retrieval}}, 
+      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}