Token Classification
Transformers
PyTorch
Safetensors
Korean
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-ko 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-ko 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-ko")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ko") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ko") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Korean
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-ko")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ko")
- Downloads last month
- 6
Space using wietsedv/xlm-roberta-base-ft-udpos28-ko 1
Evaluation results
- English Test accuracy on Universal Dependencies v2.8self-reported61.700
- Dutch Test accuracy on Universal Dependencies v2.8self-reported55.900
- German Test accuracy on Universal Dependencies v2.8self-reported58.900
- Italian Test accuracy on Universal Dependencies v2.8self-reported58.700
- French Test accuracy on Universal Dependencies v2.8self-reported53.600
- Spanish Test accuracy on Universal Dependencies v2.8self-reported52.600
- Russian Test accuracy on Universal Dependencies v2.8self-reported66.400
- Swedish Test accuracy on Universal Dependencies v2.8self-reported64.000