Multilingual text genre classification model X-GENRE

  Authors

Kuzman, Taja and Ljubešić, Nikola

  Item identifier

http://hdl.handle.net/11356/1961

 Project URL

https://macocu.eu/

 Demo URL

https://huggingface.co/classla/xlm-roberta-base-multilingual-text-genre-classifier

 Referenced by

https://doi.org/10.3390/make5030059

 Date issued

2024-09-25

 Type

toolService

 Language(s)

Albanian , Catalan , Croatian , English , Icelandic , Macedonian , Modern Greek (1453-) , Slovenian , Turkish , Ukrainian

 Description

The X-GENRE classifier is a text classification model that can be used for automatic genre identification. The model classifies texts to one of 9 genre labels: Information/Explanation, News, Instruction, Opinion/Argumentation, Forum, Prose/Lyrical, Legal, Promotion and Other (refer to the provided README file for the details on the labels). The model was shown to provide high classification performance on Albanian, Catalan, Croatian, Greek, English, Icelandic, Macedonian, Slovenian, Turkish and Ukrainian, and the zero-shot cross-lingual experiments indicate that it will likely provide comparable performance on all other languages that are supported by the XLM-RoBERTa model (see Appendix in the following paper for the list of covered languages: https://arxiv.org/abs/1911.02116). The model is based on the base-sized XLM-RoBERTa model (https://huggingface.co/FacebookAI/xlm-roberta-base). It was fine-tuned on the training split of an English-Slovenian X-GENRE dataset (http://hdl.handle.net/11356/1960), comprising of around 1,800 instances of Slovenian and English texts. Fine-tuning was performed with the simpletransformers library (https://simpletransformers.ai/) and the following hyperparameters were used: Train batch size: 8 Learning rate: 1e-5 Max. sequence length: 512 Number of epochs: 15 For the optimum performance, the genre classifier should be applied to documents of sufficient length (the rule of thumb is at least 75 words), the predictions of label "Other" should be disregarded, and only predictions, predicted with confidence higher than 0.8, should be used. With these post-processing steps, the model was shown to reach macro-F1 scores of 0.92 and 0.94 on English and Slovenian test sets respectively (cross-dataset scenario), macro-F1 scores between 0.88 and 0.95 on Croatian, Macedonian, Turkish and Ukrainian, and macro-F1 scores between 0.80 and 0.85 on Albanian, Catalan, Greek, and Icelandic (zero-shot cross-lingual scenario). Refer to the provided README file for instructions with code examples on how to use the model.

 Publisher

Jožef Stefan Institute

 Acknowledgement

Connecting Europe Facility (CEF) Telecom INEA/CEF/ICT/A2020/2278341 "MaCoCu - Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages"

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

ARRS (Slovenian Research Agency) N06-0099 and FWO-G070619N "Linguistic landscape of hate speech on social media"

 Subject(s)

text classification automatic genre identification genre classification genre fine-tuned massively multilingual pretrained language model

 Collection(s)

CLARIN.SI data & tools

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