Instructions to use Tanor/sr_pner_tesla_bcx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Tanor/sr_pner_tesla_bcx with spaCy:
!pip install https://huggingface.co/Tanor/sr_pner_tesla_bcx/resolve/main/sr_pner_tesla_bcx-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("sr_pner_tesla_bcx") # Importing as module. import sr_pner_tesla_bcx nlp = sr_pner_tesla_bcx.load() - Notebooks
- Google Colab
- Kaggle
| { | |
| "lang":"sr", | |
| "name":"pner_tesla_bcx", | |
| "version":"1.0.0", | |
| "description":"sr_pner_tesla_bcx 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 XLM-R-BERTi\u0107, 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'.", | |
| "author":"Milica Ikoni\u0107 Ne\u0161i\u0107, Sa\u0161a Petalinkar, Mihailo \u0160kori\u0107, Ranka Stankovi\u0107", | |
| "email":"", | |
| "url":"https://tesla.rgf.bg.ac.rs/", | |
| "license":"CC BY-SA 3.0", | |
| "spacy_version":">=3.7.2,<3.8.0", | |
| "spacy_git_version":"a89eae928", | |
| "vectors":{ | |
| "width":0, | |
| "vectors":0, | |
| "keys":0, | |
| "name":null | |
| }, | |
| "labels":{ | |
| "transformer":[ | |
| ], | |
| "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" | |
| ] | |
| }, | |
| "pipeline":[ | |
| "transformer", | |
| "tagger", | |
| "ner" | |
| ], | |
| "components":[ | |
| "transformer", | |
| "tagger", | |
| "ner" | |
| ], | |
| "disabled":[ | |
| ], | |
| "performance":{ | |
| "tag_acc":0.9853440156, | |
| "ents_f":0.9603720178, | |
| "ents_p":0.9567611119, | |
| "ents_r":0.9640102828, | |
| "ents_per_type":{ | |
| "ROLE":{ | |
| "p":0.8674171357, | |
| "r":0.9107025607, | |
| "f":0.8885329917 | |
| }, | |
| "PERS":{ | |
| "p":0.9881560903, | |
| "r":0.991369606, | |
| "f":0.9897602398 | |
| }, | |
| "LOC":{ | |
| "p":0.9631551635, | |
| "r":0.9783869267, | |
| "f":0.9707112971 | |
| }, | |
| "DEMO":{ | |
| "p":0.9299065421, | |
| "r":0.9401574803, | |
| "f":0.9350039154 | |
| }, | |
| "ORG":{ | |
| "p":0.8374816984, | |
| "r":0.781420765, | |
| "f":0.8084805654 | |
| }, | |
| "WORK":{ | |
| "p":0.6315789474, | |
| "r":0.5070422535, | |
| "f":0.5625 | |
| }, | |
| "EVENT":{ | |
| "p":0.5263157895, | |
| "r":0.625, | |
| "f":0.5714285714 | |
| } | |
| }, | |
| "transformer_loss":388.6984258415, | |
| "tagger_loss":562.6862918995, | |
| "ner_loss":332.6980463833 | |
| }, | |
| "requirements":[ | |
| "spacy-transformers>=1.3.4,<1.4.0" | |
| ] | |
| } |