ychafiqui

Information extraction from Resumes/CVs written in English Model Description This model is designed for information extraction from resumes/CVs written in English. It employs a transformer-based architecture with spaCy for named entity recognition (NER) tasks. The model aims to parse various sections of resumes, including personal details, education history, professional experience, skills, and certifications, enabling users to extract structured information for further processing or analysis. Model Details | Feature | Description | | --- | --- | | `Language` | English | | `Task` | Named Entity Recognition (NER) | | `Objective` | Information extraction from resumes/CVs | | `Spacy Components` | Transformer, Named Entity Recognition (NER) | | `Author` | Youssef Chafiqui | NER Entities The model recognizes various entities corresponding to different sections of a resume. Below are the entities used by the model: | Label | Description | | --- | --- | | 'FNAME' | First name | | 'LNAME' | Last name | | 'ADDRESS' | Address | | 'CERTIFICATION' | Certification | | 'EDUCATION' | Education section | | 'EMAIL' | Email address | | 'EXPERIENCE' | Experience section | | 'HOBBY' | Hobby | | 'HSKILL' | Hard skill | | 'LANGUAGE' | Language | | 'PHONE' | Phone number | | 'PROFILE' | Profile | | 'PROJECT' | Project section | | 'SSKILL' | Soft skill | | Type | Score | | --- | --- | | `F1 score` | 81.98 | | `Precision` | 83.33 | | `Recall` | 80.68 |