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README.md

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+# How to use the model?
+
+In order to use the model use can use the class in model.py like the example below:
+
+```Python
+from model import Net
+import torch
+import torchvision
+import torch.nn as nn
+from torchvision import transforms
+import torch.nn.functional as F
+from PIL import Image
+from transformers import AutoTokenizer, AutoModel
+
+model = Net()
+# If you use model on cpu you need the map_location part
+model.load_state_dict(torch.load("clip_model.pt", map_location=torch.device('cpu'))) 
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained("dbmdz/distilbert-base-turkish-cased")
+
+transform=transforms.Compose(
+        [
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+],
+)
+
+def predict(img,text_vec):
+  input = transform(img).unsqueeze(0)
+  token_list = tokenizer(text_vec,padding = True)
+
+  text = torch.Tensor(token_list["input_ids"]).long()
+  mask = torch.Tensor(token_list["attention_mask"]).long()
+
+
+  image_vec, text_vec = model(input, text , mask) 
+  print(F.softmax(torch.matmul(image_vec,text_vec.T),dim=1))
+
+img = Image.open("dog.png") # A dog image
+
+text_vec = ["Çimenler içinde bir köpek.","Bir köpek.","Çimenler içinde bir kuş."] # Descriptions
+predict(img,text_vec) # Probabilities for each description
+
+```
+
+