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| from sentence_transformers import util | |
| def compute_similarity(resume_embeds, jd_embeds): | |
| expected_sections = ["Experience & Achievements", "Skills & Competencies", "Culture Fit", "Personal Traits", "Education"] | |
| similarity_scores = {} | |
| for section in expected_sections: | |
| if section in resume_embeds and section in jd_embeds: | |
| similarity_scores[section] = float(util.cos_sim(resume_embeds[section], jd_embeds[section])) | |
| else: | |
| resume_values = list(resume_embeds.values()) | |
| jd_values = list(jd_embeds.values()) | |
| if len(resume_values) == len(jd_values): | |
| resume_section_idx = expected_sections.index(section) | |
| similarity_scores[section] = float(util.cos_sim(resume_values[resume_section_idx], jd_values[resume_section_idx])) | |
| return similarity_scores | |