sr_pner_tesla_bbmc / README.md
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metadata
tags:
  - spacy
  - token-classification
language:
  - sr
license: cc-by-sa-3.0
model-index:
  - name: sr_pner_tesla_bbmc
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.948846336
          - name: NER Recall
            type: recall
            value: 0.9486537681
          - name: NER F Score
            type: f_score
            value: 0.9487500423
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.9807907809

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