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---
tags:
- spacy
- token-classification
language:
- sr
license: cc-by-sa-3.0
model-index:
- name: sr_pner_tesla_j355
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9516940624
- name: NER Recall
type: recall
value: 0.9596130429
- name: NER F Score
type: f_score
value: 0.9556371476
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9841723761
---
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'.
| Feature | Description |
| --- | --- |
| **Name** | `sr_pner_tesla_j355` |
| **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](https://tesla.rgf.bg.ac.rs/) |
### Label Scheme
<details>
<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` | 98.42 |
| `ENTS_F` | 95.56 |
| `ENTS_P` | 95.17 |
| `ENTS_R` | 95.96 |
| `TRANSFORMER_LOSS` | 151439.86 |
| `TAGGER_LOSS` | 141230.81 |
| `NER_LOSS` | 84043.38 | |