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

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

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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-grc

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

Evaluation results

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

58.300
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

57.100
German Test accuracy on Universal Dependencies v2.8
self-reported

61.300
Italian Test accuracy on Universal Dependencies v2.8
self-reported

56.600
French Test accuracy on Universal Dependencies v2.8
self-reported

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

54.500
Russian Test accuracy on Universal Dependencies v2.8
self-reported

71.100
Swedish Test accuracy on Universal Dependencies v2.8
self-reported

62.900
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

59.900
Danish Test accuracy on Universal Dependencies v2.8
self-reported

61.600

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