Instructions to use Tanor/sr_pner_tesla_j355 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Tanor/sr_pner_tesla_j355 with spaCy:
!pip install https://huggingface.co/Tanor/sr_pner_tesla_j355/resolve/main/sr_pner_tesla_j355-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("sr_pner_tesla_j355") # Importing as module. import sr_pner_tesla_j355 nlp = sr_pner_tesla_j355.load() - Notebooks
- Google Colab
- Kaggle
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sr_pner_tesla_j355 is a spaCy model meticulously fine-tuned for Part-of-Speech Tagging,
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sr_pner_tesla_j355 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 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'.
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