---
license: cc-by-sa-4.0
datasets:
- cjvt/cc_gigafida
- cjvt/solar3
- cjvt/sloleks
language:
- sl
tags:
- word spelling error annotator
---

---
language: 
- sl

license: cc-by-sa-4.0
---

# SloBERTa-Incorrect-Spelling-Annotator

This SloBERTa model is designed to annotate incorrectly spelled words in text. It utilizes the following labels:

- 1: Indicates incorrectly spelled words.
- 2: Denotes cases where two words should be written together.
- 3: Suggests that a word should be written separately.

## Model Output Example

Imagine we have the following Slovenian text:

_Model vbesedilu o znači besede, v katerih se najajajo napake._

If we convert input data to format acceptable by SloBERTa model:

_Model <mask> vbesedilu <mask> o <mask> znači <mask> besede <mask> , <mask> v <mask> katerih <mask> se <mask> najajajo <mask> napake <mask> . <mask>_

The model might return the following predictions (note: predictions chosen for demonstration/explanation, not reproducibility!):

_Model 0 vbesedilu 3 o 2 znači 2 besede 0 , 0 v 0 katerih 0 se 0 najajajo 1 napake 0 . 0_

We can observe the following:
1. In the input sentence, the word `najajajo` is spelled incorrectly, so the model marks it with the token (0).
2. The word `vbesedilu` should be written as two words `v` and `besedilu`, so the model marks it with the token (3).
3. The words `o` and `znači` should be written as one word `označi`, so the model marks them with the tokens (2).

## More details

The model, along with its training and evaluation, is described in more detail in the following paper.
```
@inproceedings{neural-spell-checker,
author = {Klemen, Matej and Bo\v{z}i\v{c}, Martin and Holdt, \v{S}pela Arhar and Robnik-\v{S}ikonja, Marko},
title = {Neural Spell-Checker: Beyond Words with Synthetic Data Generation},
year = {2024},
doi = {10.1007/978-3-031-70563-2_7},
booktitle = {Text, Speech, and Dialogue: 27th International Conference, TSD 2024, Brno, Czech Republic, September 9–13, 2024, Proceedings, Part I},
pages = {85–96},
numpages = {12}
}
```

## Acknowledgement

The authors acknowledge the financial support from the Slovenian Research and Innovation Agency - research core funding No. P6-0411: Language Resources and Technologies for Slovene and research project No. J7-3159: Empirical foundations for digitally-supported development of writing skills.

## Authors

Thanks to Martin Božič, Marko Robnik-Šikonja and Špela Arhar Holdt for developing these models.