Modelpublic

Tanor/sr_pln_tesla_j355

sourceHugging Facecc-by-sa-3.0updated 3y agoView on Hugging Face

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Model Card

srplntesla_j355 is a spaCy model meticulously fine-tuned for Part-of-Speech Tagging, Lemmatization, 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'.

Feature	Description
Name	`sr_pln_tesla_j355`
Version	`1.0.0`
spaCy	`>=3.7.2,<3.8.0`
Default Pipeline	`transformer`, `tagger`, `trainable_lemmatizer`, `ner`
Components	`transformer`, `tagger`, `trainable_lemmatizer`, `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

<summary>View label scheme (23 labels for 2 components)</summary>

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`

</details>

Accuracy

Type	Score
`TAG_ACC`	98.47
`LEMMA_ACC`	98.34
`ENTS_F`	95.74
`ENTS_P`	95.64
`ENTS_R`	95.85
`TRANSFORMER_LOSS`	183572.28
`TAGGER_LOSS`	63121.95
`TRAINABLE_LEMMATIZER_LOSS`	99749.38
`NER_LOSS`	40508.31