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

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

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Safetensors

Model size

277M params

Tensor type

I64

F32

Inference Examples

Token Classification

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

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

Evaluation results

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

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

56.900
German Test accuracy on Universal Dependencies v2.8
self-reported

57.500
Italian Test accuracy on Universal Dependencies v2.8
self-reported

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

54.100
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

54.400
Russian Test accuracy on Universal Dependencies v2.8
self-reported

69.600
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

61.800
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

60.300
Danish Test accuracy on Universal Dependencies v2.8
self-reported

62.600

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