Token Classification
Transformers
PyTorch
Scottish Gaelic
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-gd 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-gd 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-gd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-gd") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-gd") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Scottish Gaelic
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-gd")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-gd")
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Space using wietsedv/xlm-roberta-base-ft-udpos28-gd 1
Evaluation results
- English Test accuracy on Universal Dependencies v2.8self-reported75.000
- Dutch Test accuracy on Universal Dependencies v2.8self-reported77.800
- German Test accuracy on Universal Dependencies v2.8self-reported76.500
- Italian Test accuracy on Universal Dependencies v2.8self-reported70.800
- French Test accuracy on Universal Dependencies v2.8self-reported74.600
- Spanish Test accuracy on Universal Dependencies v2.8self-reported78.700
- Russian Test accuracy on Universal Dependencies v2.8self-reported79.200
- Swedish Test accuracy on Universal Dependencies v2.8self-reported78.900