Instructions to use spacy/mk_core_news_sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use spacy/mk_core_news_sm with spaCy:
!pip install https://huggingface.co/spacy/mk_core_news_sm/resolve/main/mk_core_news_sm-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("mk_core_news_sm") # Importing as module. import mk_core_news_sm nlp = mk_core_news_sm.load() - Notebooks
- Google Colab
- Kaggle
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