Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-sr 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-sr 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-sr")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sr") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Serbian
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-sr")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sr")
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Space using wietsedv/xlm-roberta-base-ft-udpos28-sr 1
Evaluation results
- English Test accuracy on Universal Dependencies v2.8self-reported82.900
- Dutch Test accuracy on Universal Dependencies v2.8self-reported84.000
- German Test accuracy on Universal Dependencies v2.8self-reported82.700
- Italian Test accuracy on Universal Dependencies v2.8self-reported82.600
- French Test accuracy on Universal Dependencies v2.8self-reported83.600
- Spanish Test accuracy on Universal Dependencies v2.8self-reported87.300
- Russian Test accuracy on Universal Dependencies v2.8self-reported90.600
- Swedish Test accuracy on Universal Dependencies v2.8self-reported85.500