extracts work experience from unstructured resume text using a trained mlp model loaded with spacy

import spacy
import sys
import pickle
nlp = spacy.load('en_core_web_sm', disable=['parser', 'tagger'])

def extract_experience(text):
  doc = nlp(text)
  X =  pd.DataFrame([[ent.text, ent.start_char, ent.end_char, ent.label_] for ent in doc.ents],
                    columns = ["text", "start", "end", "label"])
  X_test = X[X["label"] == "ORG"].copy()
  X_test["tokens"] = X_test["text"].apply(lambda x: [token.text for token in x.split()])
  with open(f"model.pkl", 'rb') as f:
    model = pickle.load(f)
  y_pred = model.predict(X_test["tokens"])
  return list(zip(X_test["text"], y_pred))