Sv Core News Sm

tags:

• spacy
• token-classification language:
• sv license: cc-by-sa-4.0 model-index:
• name: sv_core_news_sm results:
  • task: name: NER type: token-classification metrics:
    • name: NER Precision type: precision value: 0.8002766252
    • name: NER Recall type: recall value: 0.703892944
    • name: NER F Score type: f_score value: 0.7489967638
  • task: name: TAG type: token-classification metrics:
    • name: TAG (XPOS) Accuracy type: accuracy value: 0.9351842401
  • task: name: POS type: token-classification metrics:
    • name: POS (UPOS) Accuracy type: accuracy value: 0.9511074819
  • task: name: MORPH type: token-classification metrics:
    • name: Morph (UFeats) Accuracy type: accuracy value: 0.9406961315
  • task: name: LEMMA type: token-classification metrics:
    • name: Lemma Accuracy type: accuracy value: 0.9489639686
  • task: name: UNLABELED_DEPENDENCIES type: token-classification metrics:
    • name: Unlabeled Attachment Score (UAS) type: f_score value: 0.818598856
  • task: name: LABELED_DEPENDENCIES type: token-classification metrics:
    • name: Labeled Attachment Score (LAS) type: f_score value: 0.7672877612
  • task: name: SENTS type: token-classification metrics:
    • name: Sentences F-Score type: f_score value: 0.9368318756

Details: https://spacy.io/models/sv#sv_core_news_sm

Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.

Label Scheme

Accuracy