Update README.md

README.md

@@ -27,12 +27,9 @@ You can use this model directly with a pipeline for masked language modeling:
 ```python
 
 >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
->>> from transformers import TextClassificationPipeline
+
 >>> tokenizer = AutoTokenizer.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu")
 >>> model = AutoModelForSequenceClassification.from_pretrained("hassan4830/xlm-roberta-base-finetuned-urdu")
->>> text = "وہ ایک برا شخص ہے"
->>> pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device = 0)
->>> pipe(text)
 
 [{'sequence': "[CLS] hello i'm a role model. [SEP]",
   'score': 0.05292855575680733,
@@ -59,12 +56,11 @@ You can use this model directly with a pipeline for masked language modeling:
 Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
-from transformers import DistilBertTokenizer, DistilBertModel
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-model = DistilBertModel.from_pretrained("distilbert-base-uncased")
-text = "Replace me by any text you'd like."
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
+>>> from transformers import TextClassificationPipeline
+>>> text = "وہ ایک برا شخص ہے"
+>>> pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device = 0)
+>>> pipe(text)
+
 ```
 
 and in TensorFlow: