dsresumatch.resume_scoring

Functions

resume_score(cleaned_text[, keywords, ...])

This function uses the results of keyword and section analyses (from evaluate_keywords and sections_check) to give a

Module Contents

dsresumatch.resume_scoring.resume_score(cleaned_text, keywords=None, use_only_supplied_keywords=False, add_benchmark_sections=[], feedback=True)[source]

This function uses the results of keyword and section analyses (from evaluate_keywords and sections_check) to give a summary and a score that indicates of the quality of the resume text compared to the predefined baseline. The score is calculated using the formula: ((Total No. of Sections - Missing No. of Sections) + (Total No. of Keywords - Missing No. of Section)) / (Total No. of Keywords + Total No. of Sections) * 100

Users can provide their own keywords or combine them with a default set of predefined keywords. Users can also provide names of additional sections to be checked.

Parameters:

  • cleaned_text (str) – The cleaned text content of the resume.

  • keywords (list of str, optional) – A list of keywords to compare against the resume content. If not provided, only the baseline keywords will be used. If use_only_supplied_keywords is set to True without supplying keywords, no keywords will be used, and the function will return an empty result.

  • use_only_supplied_keywords (bool, optional) – A flag to determine whether to use only the supplied keywords or to combine them with a default set of predefined keywords. Defaults to False.

  • add_benchmark_sections (list of str or str, optional) – A list of additional section names (e.g., “Skills”, “Education”, “Work Experience”, “Contact”) or a single section name as a string. Defaults to None. If a single string is provided, it will be treated as a list with one element.

  • feedback (bool, optional) – A flag to determine whether the summary should be included in the results (True), or just a line with the score should be given (False). The default is True.

Returns:

A string with a summary that gives the score of the given resume content. If feedback is True, lines explaining missing sections and keywords are also given.

Return type:

str

Examples

>>> resume_text = '''
... Jane Doe
... Contact Information: janedoe@example.com
... Education: Master of Science in Data Science (2021)
... Work Experience: Data Scientist at Big Data Inc. (2022 - Present)
... Skills: Python, Machine Learning, SQL, Data Visualization
... '''
>>> custom_keywords = ["Python", "Machine Learning", "SQL", "Big Data", "Cloud Computing"]
>>> custom_sections = ["Certifications", "Projects"]
>>> print(resume_score(
...     cleaned_text=resume_text,
...     keywords=custom_keywords,
...     use_only_supplied_keywords=False,
...     add_benchmark_sections=custom_sections
... ))

This resume attained a score of 81.3. Feedback: - Missing Keywords: ‘Big Data’, ‘Cloud Computing’ - Missing Sections: ‘Certifications’, ‘Projects’