metadata
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_skillner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.5919354839
- name: NER Recall
type: recall
value: 0.5758368201
- name: NER F Score
type: f_score
value: 0.5837751856
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 |