Update README.md

README.md

@@ -1,10 +1,40 @@
 ---
 license: mit
 ---
-
+## Overview
 The model takes a news article and predicts if it true or fake.
-The format should be:  
+The format of the input should be:  
 
 ```
 <title> TITLE HERE <content> CONTENT HERE <end>
 ```
+
+## Using this model in your code:  
+To use this model, first download it from the hugginface website:  
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
+
+model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
+``` 
+
+Then, make a prediction like follows:  
+```python
+import torch
+def predict_fake(title,text):
+    input_str = "<title>" + title + "<content>" +  text + "<end>"
+    input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt")
+    device =  'cuda' if torch.cuda.is_available() else 'cpu'
+    model.to(device)
+    with torch.no_grad():
+        output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
+    return dict(zip(["Fake","Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])] ))
+    
+print(predict_fake(<HEADLINE-HERE>,<CONTENT-HERE>))
+```
+You can also use Gradio to test the model on real-time:  
+ ```python  
+ import gradio as gr
+ iface = gr.Interface(fn=predict_fake, inputs=[gr.inputs.Textbox(lines=1,label="headline"),gr.inputs.Textbox(lines=6,label="content")], outputs="label").launch(share=True)
+ ```