Update README.md

README.md

@@ -24,10 +24,21 @@ tags:
 
 ## Model description
 Model takes as input two strings. String1 is NER label. String1 must be phrase for entity. String2 is short text where String1 is searched for semantically.
-model outputs list of zeros and ones corresponding to the occurance of NER and corresponing to tokens(tokens given by transformer tokenizer) of the Sring2, not to words.
+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
+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
+model = BertForTokenClassification.from_pretrained(modelname, num_labels=2)
+prediction_logits=model(**encodings)
+
 ## Code availibility
 
 Code used for training and testing the model is available at https://github.com/br-ai-ns-institute/Zero-ShotNER