Details: https://spacy.io/models/mk#mk_core_news_sm
Macedonian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
Feature | Description |
---|---|
Name | mk_core_news_sm |
Version | 3.7.0 |
spaCy | >=3.7.0,<3.8.0 |
Default Pipeline | morphologizer , parser , attribute_ruler , lemmatizer , ner |
Components | morphologizer , parser , senter , attribute_ruler , lemmatizer , ner |
Vectors | 0 keys, 0 unique vectors (0 dimensions) |
Sources | Macedonian Corpus (Damjan Zlatinov, Melanija Gerasimovska, Borijan Georgievski, Marija Todosovska) spaCy lookups data (Explosion) |
License | CC BY-SA 4.0 |
Author | Explosion |
Label Scheme
View label scheme (54 labels for 3 components)
Component | Labels |
---|---|
morphologizer |
POS=PROPN , POS=AUX , POS=ADJ , POS=NOUN , POS=ADP , POS=PUNCT , POS=CONJ , POS=NUM , POS=VERB , POS=PRON , POS=ADV , POS=SCONJ , POS=PART , POS=SYM , _ , POS=SPACE , POS=X , POS=INTJ |
parser |
ROOT , advmod , att , aux , cc , dep , det , dobj , iobj , neg , nsubj , pobj , poss , pozm , pozv , prep , punct , relcl |
ner |
CARDINAL , DATE , EVENT , FAC , GPE , LANGUAGE , LAW , LOC , MONEY , NORP , ORDINAL , ORG , PERCENT , PERSON , PRODUCT , QUANTITY , TIME , WORK_OF_ART |
Accuracy
Type | Score |
---|---|
TOKEN_ACC |
100.00 |
TOKEN_P |
100.00 |
TOKEN_R |
100.00 |
TOKEN_F |
100.00 |
SENTS_P |
76.06 |
SENTS_R |
70.13 |
SENTS_F |
72.97 |
DEP_UAS |
64.64 |
DEP_LAS |
47.54 |
ENTS_P |
72.65 |
ENTS_R |
70.98 |
ENTS_F |
71.80 |
POS_ACC |
91.99 |
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Evaluation results
- NER Precisionself-reported0.726
- NER Recallself-reported0.710
- NER F Scoreself-reported0.718
- POS (UPOS) Accuracyself-reported0.920
- Unlabeled Attachment Score (UAS)self-reported0.646
- Labeled Attachment Score (LAS)self-reported0.475
- Sentences F-Scoreself-reported0.730