The Trankit model for linguistic processing of standard Slovenian

  Authors

Krsnik, Luka and Dobrovoljc, Kaja

  Item identifier

http://hdl.handle.net/11356/1870

 Project URL

https://github.com/clarinsi/trankit-train

 Referenced by

https://arxiv.org/pdf/2101.03289.pdf

 Date issued

2023-09-29

 Type

toolService

 Language(s)

Slovenian

 Description

This is a retrained Slovenian standard model for Trankit v1.1.1 library (https://pypi.org/project/trankit/). It is able to predict sentence segmentation, tokenization, lemmatization, language-specific morphological annotation (MULTEXT-East morphosyntactic tags), as well as universal part-of-speech tagging, feature prediction, and dependency parsing in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). The model was trained using a dataset published by Universal Dependencies in release 2.12 (https://github.com/UniversalDependencies/UD_Slovenian-SSJ/tree/r2.12). Due to the larger training dataset compared to the original Trankit v1.1.1 model, this version yields superior results and achieves state-of-the art parsing performance for Slovenian (https://slobench.cjvt.si/leaderboard/view/11). To utilize this model, please follow the instructions provided in our github repository (https://github.com/clarinsi/trankit-train) or refer to the Trankit documentation (https://trankit.readthedocs.io/en/latest/training.html#loading). This ZIP file contains models for both xlm-roberta-large (which delivers better performance but requires more hardware resources) and xlm-roberta-base.

 Publisher

Centre for Language Resources and Technologies, University of Ljubljana

 Acknowledgement

ARRS (Slovenian Research Agency) P6-0411 "Language Resources and Technologies for Slovene"

ARRS (Slovenian Research Agency) Z6-4617 "A Treebank-Driven Approach to the Study of Spoken Slovenian"

 Subject(s)

language model lemmatisation tokenisation sentence segmentation part-of-speech tagging feature prediction parsing

 Collection(s)

CLARIN.SI data & tools

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