---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_egw_entity_extractor
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.892504864
    - name: NER Recall
      type: recall
      value: 0.8951781257
    - name: NER F Score
      type: f_score
      value: 0.8938394961
  - task:
      name: TAG
      type: token-classification
    metrics:
    - name: TAG (XPOS) Accuracy
      type: accuracy
      value: 0.9731235568
  - task:
      name: UNLABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Unlabeled Attachment Score (UAS)
      type: f_score
      value: 0.9383889999
  - task:
      name: LABELED_DEPENDENCIES
      type: token-classification
    metrics:
    - name: Labeled Attachment Score (LAS)
      type: f_score
      value: 0.9226387112
  - task:
      name: SENTS
      type: token-classification
    metrics:
    - name: Sentences F-Score
      type: f_score
      value: 0.9496868166
---
| Feature | Description |
| --- | --- |
| **Name** | `en_egw_entity_extractor` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser`, `ner` |
| **Components** | `transformer`, `tagger`, `parser`, `ner` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |

### Label Scheme

<details>

<summary>View label scheme (100 labels for 3 components)</summary>

| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`ner`** | `DATE`, `GPE`, `LOC`, `PERSON`, `REFERENCE` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `TAG_ACC` | 97.31 |
| `DEP_UAS` | 93.84 |
| `DEP_LAS` | 92.26 |
| `SENTS_P` | 95.17 |
| `SENTS_R` | 94.77 |
| `SENTS_F` | 94.97 |
| `ENTS_F` | 89.38 |
| `ENTS_P` | 89.25 |
| `ENTS_R` | 89.52 |
| `TRANSFORMER_LOSS` | 10291319.95 |
| `TAGGER_LOSS` | 1200598.50 |
| `PARSER_LOSS` | 5348475.42 |
| `NER_LOSS` | 297327.96 |