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wietsedv
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xlm-roberta-base-ft-udpos28-sme

Token Classification
Transformers
PyTorch
Northern Sami
xlm-roberta
part-of-speech
Eval Results
Inference Endpoints
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XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: North Sami

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-sme")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme")
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Inference API
Token Classification
This model can be loaded on Inference API (serverless).

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

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

Evaluation results

  • English Test accuracy on Universal Dependencies v2.8
    self-reported
    48.100
  • Dutch Test accuracy on Universal Dependencies v2.8
    self-reported
    49.500
  • German Test accuracy on Universal Dependencies v2.8
    self-reported
    40.400
  • Italian Test accuracy on Universal Dependencies v2.8
    self-reported
    48.900
  • French Test accuracy on Universal Dependencies v2.8
    self-reported
    43.900
  • Spanish Test accuracy on Universal Dependencies v2.8
    self-reported
    47.100
  • Russian Test accuracy on Universal Dependencies v2.8
    self-reported
    57.300
  • Swedish Test accuracy on Universal Dependencies v2.8
    self-reported
    47.900
  • Norwegian Test accuracy on Universal Dependencies v2.8
    self-reported
    45.500
  • Danish Test accuracy on Universal Dependencies v2.8
    self-reported
    50.700
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