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def create_skillset_dict(resume_names, resume_texts):
'''Create a dictionary containing a set of the extracted skills. Name is key, matching skillset is value'''
skillsets = [create_skill_set(resume_text) for resume_text in resume_texts]
return dict(zip(resume_names, skillsets))
def match_skills(vacature_set, cv_set, resume_name):
'''Get intersection of resume skills and job offer skills and return match percentage'''
if len(vacature_set) < 1:
print('could not extract skills from job offer text')
else:
pct_match = round(len(vacature_set.intersection(cv_set[resume_name])) / len(vacature_set) * 100, 0)
print(resume_name + " has a {}% skill match on this job offer".format(pct_match))
print('Required skills: {} '.format(vacature_set))
print('Matched skills: {} \n'.format(vacature_set.intersection(skillset_dict[resume_name])))
return (resume_name, pct_match)
add_newruler_to_pipeline(skill_pattern_path)
resume_texts, resume_names = create_tokenized_texts_list(extension)
skillset_dict = create_skillset_dict(resume_names, resume_texts)
# example of job offer text (string). Can input your own.
vacature_text = vacatures_df[vacatures_df['soort_vacature'] == 'Data Scientist'].skills.iloc[13]
# Create a set of the skills extracted from the job offer text
vacature_skillset = create_skill_set(nlp(vacature_text))
# Create a list with tuple pairs containing the names of the candidates and their match percentage
match_pairs = [match_skills(vacature_skillset, skillset_dict, name) for name in skillset_dict.keys()]
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