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wietsedv
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xlm-roberta-base-ft-udpos28-fo

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

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-fo")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fo")
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8
Safetensors
Model size
277M params
Tensor type
I64
·
F32
·
Inference API
Token Classification
This model can be loaded on Inference API (serverless).

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

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

Evaluation results

  • English Test accuracy on Universal Dependencies v2.8
    self-reported
    86.400
  • Dutch Test accuracy on Universal Dependencies v2.8
    self-reported
    83.200
  • German Test accuracy on Universal Dependencies v2.8
    self-reported
    83.200
  • Italian Test accuracy on Universal Dependencies v2.8
    self-reported
    83.200
  • French Test accuracy on Universal Dependencies v2.8
    self-reported
    80.600
  • Spanish Test accuracy on Universal Dependencies v2.8
    self-reported
    83.400
  • Russian Test accuracy on Universal Dependencies v2.8
    self-reported
    83.600
  • Swedish Test accuracy on Universal Dependencies v2.8
    self-reported
    87.300
  • Norwegian Test accuracy on Universal Dependencies v2.8
    self-reported
    83.900
  • Danish Test accuracy on Universal Dependencies v2.8
    self-reported
    87.500
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