Update README.md

README.md

@@ -1,50 +1,50 @@
----
-license: mit
-widget:
-- text: "Some ninja attacked the White House."
-  example_title: "Fake example 1"
-language:
-- en
-tags:
-- classification
-datasets:
-- "fake-and-real-news-dataset on kaggle"
----
-## Overview
-The model is a `roberta-base` fine-tuned on the [fake-and-real-news-dataset dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset). It has a 100% accuracy on that dataset. 
-The model takes a news article and predicts if it is true or fake.
-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)
+---
+license: mit
+widget:
+- text: "Some ninja attacked the White House."
+  example_title: "Fake example 1"
+language:
+- en
+tags:
+- classification
+datasets:
+- "fake-and-real-news-dataset on kaggle"
+---
+## Overview
+The model is a `roberta-base` fine-tuned on [fake-and-real-news-dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset). It has a 100% accuracy on that dataset. 
+The model takes a news article and predicts if it is true or fake.
+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)
  ```