sr_ner_tesla_j355 is a spaCy model meticulously fine-tuned for Named Entity Recognition in Serbian language texts. This advanced model incorporates a transformer layer based on Jerteh-355, 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_ner_tesla_j355`
Version	`1.0.0`
spaCy	`>=3.7.2,<3.8.0`
Default Pipeline	`transformer`, `ner`
Components	`transformer`, `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 (7 labels for 1 components)

Component	Labels
`ner`	`DEMO`, `EVENT`, `LOC`, `ORG`, `PERS`, `ROLE`, `WORK`

Accuracy

Type	Score
`ENTS_F`	95.40
`ENTS_P`	95.06
`ENTS_R`	95.74
`TRANSFORMER_LOSS`	137907.16
`NER_LOSS`	265590.02

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Evaluation results

NER Precision
self-reported

0.951
NER Recall
self-reported

0.957
NER F Score
self-reported

0.954