A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts.
Future developments or maintainence of this model by Nesta have been stopped as of May 2025.
| Feature | Description | 
|---|---|
| Name | en_skillner | 
| Version | 3.7.1 | 
| spaCy | >=3.7.4,<3.8.0 | 
| Default Pipeline | tok2vec,tagger,parser,attribute_ruler,lemmatizer,ner | 
| Components | tok2vec,tagger,parser,senter,attribute_ruler,lemmatizer,ner | 
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) | 
| Sources | OntoNotes 5 (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston) ClearNLP Constituent-to-Dependency Conversion (Emory University) WordNet 3.0 (Princeton University) Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl) (Explosion) | 
| License | MIT | 
| Author | nestauk | 
Label Scheme
View label scheme (3 labels for 1 components)
| Component | Labels | 
|---|---|
| ner | SKILL,EXPERIENCE,BENEFIT | 
Accuracy
| Type | Score | 
|---|---|
| ENTS_P | 59.19 | 
| ENTS_R | 57.58 | 
| ENTS_F | 58.38 | 
| SKILL_P | 72.19 | 
| SKILL_R | 72.62 | 
| SKILL_F | 72.40 | 
| EXPERIENCE_P | 52.14 | 
| EXPERIENCE_R | 41.48 | 
| EXPERIENCE_F | 46.20 | 
| BENEFIT_P | 75.61 | 
| BENEFIT_R | 46.27 | 
| BENEFIT_F | 57.41 | 
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Evaluation results
- NER Precisionself-reported0.592
- NER Recallself-reported0.576
- NER F Scoreself-reported0.584
