Tanor/sr_pner_tesla_bbmc

sr_pner_tesla_bbmc is a spaCy model meticulously fine-tuned for Part-of-Speech Tagging and Named Entity Recognition in Serbian language texts. This advanced model incorporates a transformer layer based on bert-base-multilingual-cased, enhancing its analytical capabilities. It is proficient in identifying 7 distinct categories of entities: PERS (persons), ROLE (professions), DEMO (demonyms), ORG (organizations), LOC (locations), WORK (artworks), and EVENT (events). Detailed information about these categories is available in the accompanying table. The development of this model has been made possible through the support of the Science Fund of the Republic of Serbia, under grant #7276, for the project 'Text Embeddings - Serbian Language Applications - TESLA'.

Feature	Description
Name	sr_pner_tesla_bbmc
Version	1.0.0
spaCy	>=3.7.2,<3.8.0
Default Pipeline	transformer, tagger, ner
Components	transformer, tagger, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	n/a
License	CC BY-SA 3.0
Author	Milica Ikonić Nešić, Saša Petalinkar, Mihailo Škorić, Ranka Stanković

Label Scheme

View label scheme (23 labels for 2 components)

Component	Labels
tagger	ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, VERB, X
ner	DEMO, EVENT, LOC, ORG, PERS, ROLE, WORK

Accuracy

Type	Score
TAG_ACC	98.08
ENTS_F	94.88
ENTS_P	94.88
ENTS_R	94.87
TRANSFORMER_LOSS	378998.39
TAGGER_LOSS	505917.93
NER_LOSS	205095.28