readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - tr
  license: cc-by-sa-4.0
  model-index:
  - name: tr_core_news_lg
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8953552753
      - name: NER Recall
        type: recall
        value: 0.8828096567
      - name: NER F Score
        type: f_score
        value: 0.889038209
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.9119084416
    - task:
        name: POS
        type: token-classification
      metrics:
      - name: POS (UPOS) Accuracy
        type: accuracy
        value: 0.9067747055
    - task:
        name: MORPH
        type: token-classification
      metrics:
      - name: Morph (UFeats) Accuracy
        type: accuracy
        value: 0.891348845
    - task:
        name: LEMMA
        type: token-classification
      metrics:
      - name: Lemma Accuracy
        type: accuracy
        value: 0.8231760731
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.7348022033
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.6372603014
    - task:
        name: SENTS
        type: token-classification
      metrics:
      - name: Sentences F-Score
        type: f_score
        value: 0.8446550816
  ---
  Turkish large sized pipeline for TrSpaCy. Components: tok2vec, tagger, morphologizer, lemmatizer, parser, ner
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `tr_core_news_lg` |
  | **Version** | `3.4.2` |
  | **spaCy** | `>=3.4.2,<3.5.0` |
  | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` |
  | **Components** | `tok2vec`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` |
  | **Vectors** | -1 keys, 200000 unique vectors (300 dimensions) |
  | **Sources** | [UD Turkish BOUN](https://github.com/UniversalDependencies/UD_Turkish-BOUN) (Türk, Utku; Atmaca, Furkan; Özateş, Şaziye Betül; Berk, Gözde; Bedir, Seyyit Talha; Köksal, Abdullatif; Öztürk Başaran, Balkız; Güngör, Tunga; Özgür, Arzucan)<br />[Turkish Wiki NER dataset](https://github.com/turkish-nlp-suite/NER-datasets/tree/main/Turkish-Wiki-NER-Dataset) (Duygu Altinok, Co-one Istanbul)<br />[PANX/WikiANN](http://hlt.sztaki.hu/resources/hunnerwiki.html) (Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, Heng Ji)<br />[Large-sized Turkish Floret word vectors (MC4 corpus)](https://huggingface.co/turkish-nlp-suite/tr_vectors_web_lg) (Duygu Altinok) |
  | **License** | `cc-by-sa-4.0` |
  | **Author** | [Duygu Altinok](https://github.com/turkish-nlp-suite/turkish-spacy-models) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (1552 labels for 3 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`tagger`** | `ADP`, `ADV`, `ANum`, `ANum_Adj`, `ANum_Ness`, `ANum_Noun`, `ANum_With`, `ANum_Zero`, `Abr`, `Abr_With`, `Adj`, `Adj_Ness`, `Adj_With`, `Adj_Without`, `Adj_Zero`, `Adv`, `Adverb`, `Adverb_Adverb`, `Adverb_Noun`, `Adverb_Zero`, `Conj`, `Conj_Conj`, `DET`, `Demons`, `Demons_Zero`, `Det`, `Det_Zero`, `Dup`, `Interj`, `NAdj`, `NAdj_Aux`, `NAdj_Ness`, `NAdj_Noun`, `NAdj_Rel`, `NAdj_Verb`, `NAdj_With`, `NAdj_Without`, `NAdj_Zero`, `NNum`, `NNum_Rel`, `NNum_Zero`, `NOUN`, `Neg`, `Ness`, `Noun`, `Noun_Ness`, `Noun_Noun`, `Noun_Rel`, `Noun_Since`, `Noun_Verb`, `Noun_With`, `Noun_With_Ness`, `Noun_With_Verb`, `Noun_With_Zero`, `Noun_Without`, `Noun_Zero`, `PCAbl`, `PCAbl_Rel`, `PCAcc`, `PCDat`, `PCDat_Zero`, `PCGen`, `PCIns`, `PCIns_Zero`, `PCNom`, `PCNom_Adj`, `PCNom_Noun`, `PCNom_Zero`, `PRON`, `PUNCT`, `Pers`, `Pers_Ness`, `Pers_Pers`, `Pers_Rel`, `Pers_Zero`, `Postp`, `Prop`, `Prop_Conj`, `Prop_Rel`, `Prop_Since`, `Prop_With`, `Prop_Zero`, `Punc`, `Punc_Noun_Ness`, `Punc_Noun_Rel`, `Quant`, `Quant_Zero`, `Ques`, `Ques_Zero`, `Reflex`, `Reflex_Zero`, `Rel`, `SYM`, `Since`, `Since_Since`, `Verb`, `Verb_Conj`, `Verb_Ness`, `Verb_Noun`, `Verb_Verb`, `Verb_With`, `Verb_Zero`, `With`, `Without`, `Without_Zero`, `Zero` |
  | **`morphologizer`** | `NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Mood=Ind\|Number=Plur,Sing\|Number[psor]=Sing\|POS=NOUN\|Person=1,3\|Person[psor]=3\|Tense=Pres`, `POS=PUNCT`, `POS=ADV`, `POS=NOUN`, `Case=Nom\|Number=Sing\|POS=ADJ\|Person=3`, `POS=DET`, `Case=Loc\|Number=Sing\|POS=VERB\|Person=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3`, `POS=ADJ`, `Aspect=Perf\|Case=Nom\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|VerbForm=Part`, `Case=Gen\|Number=Sing\|POS=NOUN\|Person=3`, `POS=PRON`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=3`, `Aspect=Perf\|Case=Acc\|Number=Sing\|Number[psor]=Sing\|POS=VERB\|Person=3\|Person[psor]=3\|Polarity=Pos\|Tense=Past\|V