Update README.md

README.md

@@ -93,3 +93,22 @@ The following hyperparameters were used during training:
 - Pytorch 2.1.2+cu121
 - Datasets 2.10.1
 - Tokenizers 0.15.0
+
+### How to use?
+```python
+# Both models generate vectors with 768 dimensions.
+from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor
+# download pre-trained models
+vision_encoder = CLIPVisionModel.from_pretrained('SeyedAli/persian-clip')
+preprocessor = CLIPFeatureExtractor.from_pretrained('SeyedAli/persian-clip')
+text_encoder = RobertaModel.from_pretrained('SeyedAli/persian-clip')
+tokenizer = AutoTokenizer.from_pretrained('SeyedAli/persian-clip')
+# define input image and input text
+text = 'something'
+image = PIL.Image.open('my_favorite_image.jpg')
+# compute embeddings
+text_embedding = text_encoder(**tokenizer(text,
+                                          return_tensors='pt')).pooler_output
+image_embedding = vision_encoder(**preprocessor(image, 
+                                                return_tensors='pt')).pooler_output
+```