Token Classification
Transformers
PyTorch
Ukrainian
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/xlm-roberta-base-ft-udpos28-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wietsedv/xlm-roberta-base-ft-udpos28-uk")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-uk") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-uk") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Ukrainian
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-uk")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-uk")
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Evaluation results
- English Test accuracy on Universal Dependencies v2.8self-reported82.200
- Dutch Test accuracy on Universal Dependencies v2.8self-reported84.300
- German Test accuracy on Universal Dependencies v2.8self-reported82.400
- Italian Test accuracy on Universal Dependencies v2.8self-reported83.900
- French Test accuracy on Universal Dependencies v2.8self-reported82.600
- Spanish Test accuracy on Universal Dependencies v2.8self-reported86.200
- Russian Test accuracy on Universal Dependencies v2.8self-reported93.300
- Swedish Test accuracy on Universal Dependencies v2.8self-reported86.300