Update README.md

README.md

@@ -27,7 +27,7 @@ More information needed
 The model can be used for NLI tasks related to biomedical data and even be adapted to fact-checking tasks. It can be used from the Huggingface pipeline method as follows:
 
 
-```
+```python
 from transformers import TextClassificationPipeline, AutoModel, AutoTokenizer, AutoModelForSequenceClassification
 model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI", num_labels=3, id2label = {1: 'entailment', 0: 'contradiction',2:'neutral'})
 tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI")
@@ -39,7 +39,7 @@ pipe(['ALDH1 expression is associated with better breast cancer outcomes',
 
 The output for the above will be:
 
-```
+```python
 [[{'label': 'contradiction', 'score': 0.10193759202957153},
   {'label': 'entailment', 'score': 0.2933262586593628},
   {'label': 'neutral', 'score': 0.6047361493110657}],