metadata

widget:
  - text: ' cvdqs qdfvfsd dfq '
tags:
  - spacy
  - token-classification
  - cv
  - resume parsing
  - resume extraction
  - named entity recognition
language:
  - en
  - fr
model-index:
  - name: en_cv_info_extr
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.8333333333
          - name: NER Recall
            type: recall
            value: 0.8067729084
          - name: NER F Score
            type: f_score
            value: 0.8198380567
library_name: spacy
pipeline_tag: token-classification

Information extraction from Resumes/CVs written in English

Feature	Description
Name	`en_cv_info_extr`
Version	`0.0.0`
spaCy	`>=3.6.1,<3.7.0`
Default Pipeline	`transformer`, `ner`
Components	`transformer`, `ner`
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	n/a
License	n/a
Author	Youssef Chafiqui

Label Scheme

View label scheme (14 labels for 1 components)

Component	Labels
`ner`	`ADDRESS`, `CERTIFICATION`, `EDUCATION`, `EMAIL`, `EXPERIENCE`, `FNAME`, `HOBBY`, `HSKILL`, `LANGUAGE`, `LNAME`, `PHONE`, `PROFILE`, `PROJECT`, `SSKILL`

Accuracy

Type	Score
`ENTS_F`	81.98
`ENTS_P`	83.33
`ENTS_R`	80.68
`TRANSFORMER_LOSS`	1473.09
`NER_LOSS`	287784.88