Update code

README.md

@@ -19,6 +19,7 @@ tags:
 - medical
 - zero-shot
 - few-shot
+library_name: transformers
 ---
 # Zero and few shot NER for biomedical texts
 
@@ -27,17 +28,22 @@ Model takes as input two strings. String1 is NER label. String1 must be phrase f
 model outputs list of zeros and ones corresponding to the occurance of Named Entity and corresponing to the tokens(tokens given by transformer tokenizer) of the Sring2.
 
 ## Example of usage
-
+```
 from transformers import AutoTokenizer
-modelname='./' #modelpath
+from transformers import BertForTokenClassification
+
+modelname = 'ProdicusII/ZeroShotBioNER'  # modelpath
 tokenizer = AutoTokenizer.from_pretrained(modelname)  ## loading the tokenizer of that model
-string1='Drug'
-string2='No recent antibiotics or other nephrotoxins, and no symptoms of UTI with benign UA.'
-encodings = tokenizer(string1,string2, is_split_into_words=False,
-            padding=True, truncation=True, add_special_tokens=True, return_offsets_mapping=False,max_length=512,return_tensors='pt')
-from transformers import BertForTokenClassification #AutoModelForPreTraining
+string1 = 'Drug'
+string2 = 'No recent antibiotics or other nephrotoxins, and no symptoms of UTI with benign UA.'
+encodings = tokenizer(string1, string2, is_split_into_words=False,
+                      padding=True, truncation=True, add_special_tokens=True, return_offsets_mapping=False,
+                      max_length=512, return_tensors='pt')
+
 model = BertForTokenClassification.from_pretrained(modelname, num_labels=2)
-prediction_logits=model(**encodings)
+prediction_logits = model(**encodings)
+print(prediction_logits)
+```
 
 ## Code availibility