---
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.

| 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](https://catalog.ldc.upenn.edu/LDC2013T19) (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)<br>[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br>[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br>[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
| **License** | `MIT` |
| **Author** | [nestauk](https://explosion.ai) |

### Label Scheme

<details>

<summary>View label scheme (3 labels for 1 components)</summary>

| Component | Labels |
| --- | --- |
| **`ner`** | `SKILL`, `EXPERIENCE`, `BENEFIT` |

</details>

### 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 |