readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - en
  model-index:
  - name: en_engagement_LSTM_f5
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.0
      - name: NER Recall
        type: recall
        value: 0.0
      - name: NER F Score
        type: f_score
        value: 0.0
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.0
    - task:
        name: LEMMA
        type: token-classification
      metrics:
      - name: Lemma Accuracy
        type: accuracy
        value: 0.0
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.0
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.0
    - task:
        name: SENTS
        type: token-classification
      metrics:
      - name: Sentences F-Score
        type: f_score
        value: 0.9056497175
  ---
  | Feature | Description |
  | --- | --- |
  | **Name** | `en_engagement_LSTM_f5` |
  | **Version** | `1.0.0` |
  | **spaCy** | `>=3.4.4,<3.5.0` |
  | **Default Pipeline** | `transformer`, `parser`, `tagger`, `ner`, `attribute_ruler`, `lemmatizer`, `trainable_transformer`, `spancat` |
  | **Components** | `transformer`, `parser`, `tagger`, `ner`, `attribute_ruler`, `lemmatizer`, `trainable_transformer`, `spancat` |
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
  | **Sources** | n/a |
  | **License** | n/a |
  | **Author** | [n/a]() |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (122 labels for 4 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`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` |
  | **`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`, ```` |
  | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
  | **`spancat`** | `ATTRIBUTION`, `ENTERTAIN`, `PROCLAIM`, `COUNTER`, `CITATION`, `SOURCES`, `JUSTIFYING`, `MONOGLOSS`, `DENY`, `ENDOPHORIC` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `DEP_UAS` | 0.00 |
  | `DEP_LAS` | 0.00 |
  | `DEP_LAS_PER_TYPE` | 0.00 |
  | `SENTS_P` | 90.21 |
  | `SENTS_R` | 90.92 |
  | `SENTS_F` | 90.56 |
  | `TAG_ACC` | 0.00 |
  | `ENTS_F` | 0.00 |
  | `ENTS_P` | 0.00 |
  | `ENTS_R` | 0.00 |
  | `LEMMA_ACC` | 0.00 |
  | `SPANS_SC_F` | 77.22 |
  | `SPANS_SC_P` | 78.86 |
  | `SPANS_SC_R` | 75.64 |
  | `TRAINABLE_TRANSFORMER_LOSS` | 525.22 |
  | `SPANCAT_LOSS` | 96507.54 |