Update README.md

README.md

@@ -23,16 +23,7 @@ labelint = ['LABEL_0', 'LABEL_1', 'LABEL_2', 'LABEL_3', 'LABEL_4', 'LABEL_5', 'L
 
 labeltxt = np.loadtxt("TASK2/label_vals/l1.txt", dtype="str")
 
-(where labeltxt is : Agent
-Device
-Event
-Place
-Species
-SportsSeason
-TopicalConcept
-UnitOfWork
-Work
-)
+(where labeltxt is : Agent, Device, Event, Place, Species, SportsSeason, TopicalConcept, UnitOfWork, Work)
 
 
 
@@ -40,30 +31,23 @@ Work
 ### How to use
 
 You can use this model directly with a pipeline:
-
-from transformers import pipeline
-import numpy as np
-
-text = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three."
-
-
-classifier = pipeline("text-classification", model="carbonnnnn/T2L1DISTILBERT")
-
-
-labeltxt = np.loadtxt("TASK2/label_vals/l1.txt", dtype="str")
-
-
-labelint = ['LABEL_0', 'LABEL_1', 'LABEL_2', 'LABEL_3', 'LABEL_4', 'LABEL_5', 'LABEL_6', 'LABEL_7', 'LABEL_8']
-
-
-output = classifier(text)[0]['label']
-
-
-for i in range(len(labelint)):
-
-    if output == labelint[i]:
-    
-      print("Output is : " + str(labeltxt[i])) 
+```python
+  from transformers import pipeline
+  import numpy as np
+  
+  text = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three."
+  classifier = pipeline("text-classification", model="carbonnnnn/T2L1DISTILBERT")
+  labeltxt = np.loadtxt("TASK2/label_vals/l1.txt", dtype="str")
+  labelint = ['LABEL_0', 'LABEL_1', 'LABEL_2', 'LABEL_3', 'LABEL_4', 'LABEL_5', 'LABEL_6', 'LABEL_7', 'LABEL_8']
+  
+  output = classifier(text)[0]['label']
+  
+  for i in range(len(labelint)):
+      if output == labelint[i]:
+        print("Output is : " + str(labeltxt[i])) 
+
+```
+      
 
 ### Limitations and bias
 
@@ -76,4 +60,3 @@ predictions. It also inherits some of
 
 
 
-### BibTeX entry and citation info