A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts.
| 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 |
46.06 |
ENTS_R |
45.74 |
ENTS_F |
45.90 |
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Evaluation results
- NER Precisionself-reported0.461
- NER Recallself-reported0.457
- NER F Scoreself-reported0.459