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wietsedv
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xlm-roberta-base-ft-udpos28-tr

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

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-tr")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-tr")
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Model size
277M params
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I64
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F32
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Token Classification
Inference API (serverless) is not available, repository is disabled.

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

Spaces using wietsedv/xlm-roberta-base-ft-udpos28-tr 2

Evaluation results

  • English Test accuracy on Universal Dependencies v2.8
    self-reported
    74.400
  • Dutch Test accuracy on Universal Dependencies v2.8
    self-reported
    73.700
  • German Test accuracy on Universal Dependencies v2.8
    self-reported
    73.500
  • Italian Test accuracy on Universal Dependencies v2.8
    self-reported
    73.200
  • French Test accuracy on Universal Dependencies v2.8
    self-reported
    71.400
  • Spanish Test accuracy on Universal Dependencies v2.8
    self-reported
    71.100
  • Russian Test accuracy on Universal Dependencies v2.8
    self-reported
    77.900
  • Swedish Test accuracy on Universal Dependencies v2.8
    self-reported
    74.500
  • Norwegian Test accuracy on Universal Dependencies v2.8
    self-reported
    69.200
  • Danish Test accuracy on Universal Dependencies v2.8
    self-reported
    73.800
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