Update README.md

README.md

@@ -24,7 +24,7 @@ Related paper: [Fact-Preserved Personalized News Headline Generation](https://ie
 
 
 Example on how to calculate the FactCC score :
-```
+```python
 from transformers import BertForSequenceClassification, BertTokenizer
 model_path = 'THEATLAS/FactCC-PENS'
 
@@ -54,7 +54,7 @@ print(f"fact_scores: {fact_scores}")
 This is a more modern implementation of the model and code from [the original github repo](https://github.com/salesforce/factCC)
 
 This model is trained to predict whether a summary is factual with respect to the original text. Basic usage:
-```
+```python
 from transformers import BertForSequenceClassification, BertTokenizer
 model_path = 'THEATLAS/FactCC-PENS'
 
@@ -72,7 +72,7 @@ model.config.id2label[pred.item()] # prints: INCORRECT
 ```
 
 It can also be used with a pipeline. Beware that since pipelines are not thought to be used with pair of sentences, and you have to use this double-list hack:
-```
+```bash
 >>> from transformers import pipeline
 
 >>> pipe=pipeline(model="THEATLAS/FactCC-PENS")