XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Classical 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-lzh")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lzh")

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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-lzh

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

Evaluation results

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

33.600
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

30.900
German Test accuracy on Universal Dependencies v2.8
self-reported

31.100
Italian Test accuracy on Universal Dependencies v2.8
self-reported

31.100
French Test accuracy on Universal Dependencies v2.8
self-reported

30.300
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

30.600
Russian Test accuracy on Universal Dependencies v2.8
self-reported

37.100
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

35.600
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

32.700
Danish Test accuracy on Universal Dependencies v2.8
self-reported

35.000

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