Update README.md

README.md

@@ -22,6 +22,30 @@ The model takes a claim and corresponding evidence as input and returns a label
 
 To use the Roberta-Fact-Check Model, you can simply pass in a claim and evidence as input to the model and receive a label indicating whether the evidence supports or refutes the claim. The model can be integrated into various applications for fact-checking and misinformation detection.
 
+```python
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+# Load the tokenizer and model
+tokenizer = RobertaTokenizer.from_pretrained('Dzeniks/roberta-fact-check')
+model = RobertaForSequenceClassification.from_pretrained('Dzeniks/roberta-fact-check')
+
+# Define the claim with evidence to classify
+claim = "Albert Einstein work in the field of computer science"
+evidence = "Albert Einstein was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time."
+
+# Tokenize the claim with evidence
+x = tokenizer.encode_plus(claim, evidence, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+  prediction = model(**x)
+
+label = torch.argmax(outputs[0]).item()
+
+print(f"Label: {label}")
+```
+
 ## Acknowledgements
 
 This model was developed using the Hugging Face transformers library and trained on the FEVER and Hover datasets. We would like to thank the developers of these datasets for their contributions to the community.