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

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

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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-en

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

Evaluation results

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

96.000
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

90.400
German Test accuracy on Universal Dependencies v2.8
self-reported

88.600
Italian Test accuracy on Universal Dependencies v2.8
self-reported

87.800
French Test accuracy on Universal Dependencies v2.8
self-reported

87.400
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

90.300
Russian Test accuracy on Universal Dependencies v2.8
self-reported

91.000
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

94.000
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

89.600
Danish Test accuracy on Universal Dependencies v2.8
self-reported

91.600

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