Checksum .tar.gz:
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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 | 3.8.0
|
| spaCy | >=3.8.0,<3.9.0
|
| 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 |
| Model size | 17 MB |
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
| 56.96 |
SENTS_R
| 58.44 |
SENTS_F
| 57.69 |
ENTS_P
| 72.29 |
ENTS_R
| 71.06 |
ENTS_F
| 71.67 |
POS_ACC
| 91.93 |
DEP_UAS
| 65.88 |
DEP_LAS
| 47.70 |
Installation
pip install spacy
python -m spacy download mk_core_news_sm