Update README.md

README.md

@@ -29,21 +29,19 @@ This model is designed to:
 
 ## Example Code
 
-You can use the following code to perform operations such as getting distinct elements from a list or splitting text into sentences.
-
 ```python
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 
 tokenizer = T5Tokenizer.from_pretrained("Babelscape/t5-base-summarization-claim-extractor")
-model = T5ForConditionalGeneration.from_pretrained("Babelscape/t5-base-summarization-claim-extractor").to("cuda:0")
-device = "cuda:0"
+model = T5ForConditionalGeneration.from_pretrained("Babelscape/t5-base-summarization-claim-extractor")
 summary = 'Simone Biles made a triumphant return to the Olympic stage at the Paris 2024 Games, competing in the women’s gymnastics qualifications. Overcoming a previous struggle with the “twisties” that led to her withdrawal from events at the Tokyo 2020 Olympics, Biles dazzled with strong performances on all apparatus, helping the U.S. team secure a commanding lead in the qualifications. Her routines showcased her resilience and skill, drawing enthusiastic support from a star-studded audience'
 
-tok_input = tokenizer.batch_encode_plus([summary], return_tensors="pt", padding=True).to(device)
+tok_input = tokenizer.batch_encode_plus([summary], return_tensors="pt", padding=True)
 claims = model.generate(**tok_input)
 claims = tokenizer.batch_decode(claims, skip_special_tokens=True)
 
 ```
+**Note**: The model outputs the claims in a single string. **Kindly remember to split the string into sentences** in order to retrieve the singular claims.
 
   
 ### Training