Update README.md

README.md

@@ -24,20 +24,23 @@ This method is surprisingly effective in many cases, particularly when used with
 
 With the zero-shot classification pipeline
 The model can be loaded with the zero-shot-classification pipeline like so:
-
+```
 from transformers import pipeline
 classifier = pipeline("zero-shot-classification",
                       model="facebook/bart-large-mnli")
-You can then use this pipeline to classify sequences into any of the class names you specify.
 
+```
+You can then use this pipeline to classify sequences into any of the class names you specify.
+```
 sequence_to_classify = "one day I will see the world"
 candidate_labels = ['travel', 'cooking', 'dancing']
 classifier(sequence_to_classify, candidate_labels)
 #{'labels': ['travel', 'dancing', 'cooking'],
 # 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],
 # 'sequence': 'one day I will see the world'}
+```
 If more than one candidate label can be correct, pass multi_class=True to calculate each class independently:
-
+```
 candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
 classifier(sequence_to_classify, candidate_labels, multi_class=True)
 #{'labels': ['travel', 'exploration', 'dancing', 'cooking'],
@@ -46,7 +49,9 @@ classifier(sequence_to_classify, candidate_labels, multi_class=True)
 #  0.0057061901316046715,
 #  0.0018193122232332826],
 # 'sequence': 'one day I will see the world'}
+```
 With manual PyTorch
+```
 # pose sequence as a NLI premise and label as a hypothesis
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
@@ -64,4 +69,5 @@ logits = nli_model(x.to(device))[0]
 # "entailment" (2) as the probability of the label being true 
 entail_contradiction_logits = logits[:,[0,2]]
 probs = entail_contradiction_logits.softmax(dim=1)
-prob_label_is_true = probs[:,1]
+prob_label_is_true = probs[:,1]
+```