add cycle

extractor.py

@@ -1,6 +1,7 @@
 from transformers import RobertaTokenizerFast, AutoModelForTokenClassification
 import re
 import torch
+from itertools import cycle
 
 tokenizer = RobertaTokenizerFast.from_pretrained("mrfirdauss/robert-base-finetuned-cv")
 model = AutoModelForTokenClassification.from_pretrained("mrfirdauss/robert-base-finetuned-cv")
@@ -138,7 +139,7 @@ def predict(text):
       profile['links'].append(links['text'])
     # Process experiences and education
     print(process_tokens(data, 'EXPERIENCES DESC'))
-    for designation, company, experience_desc in zip(process_tokens(data, 'DESIGNATION'),process_tokens(data, 'COMPANY'),process_tokens(data, 'EXPERIENCES DESC') ):
+    for designation, company, experience_desc in zip(cycle(process_tokens(data, 'DESIGNATION')),cycle(process_tokens(data, 'COMPANY')),cycle(process_tokens(data, 'EXPERIENCES DESC'))):
         profile['experiences'].append({
             "start": None,
             "end": None,