Token Classification
Transformers
PyTorch
Belarusian
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-be 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-be 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-be")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-be") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-be") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Belarusian
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-be")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-be")
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Evaluation results
- English Test accuracy on Universal Dependencies v2.8self-reported77.500
- Dutch Test accuracy on Universal Dependencies v2.8self-reported80.700
- German Test accuracy on Universal Dependencies v2.8self-reported79.400
- Italian Test accuracy on Universal Dependencies v2.8self-reported80.100
- French Test accuracy on Universal Dependencies v2.8self-reported81.200
- Spanish Test accuracy on Universal Dependencies v2.8self-reported83.600
- Russian Test accuracy on Universal Dependencies v2.8self-reported95.300
- Swedish Test accuracy on Universal Dependencies v2.8self-reported85.900