XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: North Sami

This model is part of our paper called:

Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages

Check the Space for more details.

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme")

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Inference Examples

Token Classification

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Dataset used to train wietsedv/xlm-roberta-base-ft-udpos28-sme

Space using wietsedv/xlm-roberta-base-ft-udpos28-sme 1

Evaluation results

English Test accuracy on Universal Dependencies v2.8
self-reported

48.100
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

49.500
German Test accuracy on Universal Dependencies v2.8
self-reported

40.400
Italian Test accuracy on Universal Dependencies v2.8
self-reported

48.900
French Test accuracy on Universal Dependencies v2.8
self-reported

43.900
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

47.100
Russian Test accuracy on Universal Dependencies v2.8
self-reported

57.300
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

47.900
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

45.500
Danish Test accuracy on Universal Dependencies v2.8
self-reported

50.700

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