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

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

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Token Classification

This model is not currently available via any of the supported third-party Inference Providers, and the model is not deployed on the HF Inference API.

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

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

Evaluation results

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

62.800
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

63.500
German Test accuracy on Universal Dependencies v2.8
self-reported

63.800
Italian Test accuracy on Universal Dependencies v2.8
self-reported

60.200
French Test accuracy on Universal Dependencies v2.8
self-reported

58.500
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

64.900
Russian Test accuracy on Universal Dependencies v2.8
self-reported

77.200
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

68.500
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

64.600
Danish Test accuracy on Universal Dependencies v2.8
self-reported

66.100

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