Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-sa 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-sa 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-sa")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sa") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sa") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Sanskrit
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-sa")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sa")
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Evaluation results
- English Test accuracy on Universal Dependencies v2.8self-reported31.400
- Dutch Test accuracy on Universal Dependencies v2.8self-reported28.400
- German Test accuracy on Universal Dependencies v2.8self-reported32.300
- Italian Test accuracy on Universal Dependencies v2.8self-reported28.300
- French Test accuracy on Universal Dependencies v2.8self-reported28.100
- Spanish Test accuracy on Universal Dependencies v2.8self-reported28.500
- Russian Test accuracy on Universal Dependencies v2.8self-reported37.500
- Swedish Test accuracy on Universal Dependencies v2.8self-reported35.700