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

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

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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-wo

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

Evaluation results

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

51.400
Dutch Test accuracy on Universal Dependencies v2.8
self-reported

52.200
German Test accuracy on Universal Dependencies v2.8
self-reported

38.400
Italian Test accuracy on Universal Dependencies v2.8
self-reported

51.200
French Test accuracy on Universal Dependencies v2.8
self-reported

48.800
Spanish Test accuracy on Universal Dependencies v2.8
self-reported

52.400
Russian Test accuracy on Universal Dependencies v2.8
self-reported

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

49.000
Norwegian Test accuracy on Universal Dependencies v2.8
self-reported

49.100
Danish Test accuracy on Universal Dependencies v2.8
self-reported

52.400

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