Token Classification
Transformers
PyTorch
Safetensors
Portuguese
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-pt 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-pt 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-pt")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pt") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pt") - Notebooks
- Google Colab
- Kaggle
XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Portuguese
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-pt")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pt")
- Downloads last month
- 5
Space using wietsedv/xlm-roberta-base-ft-udpos28-pt 1
Evaluation results
- English Test accuracy on Universal Dependencies v2.8self-reported87.100
- Dutch Test accuracy on Universal Dependencies v2.8self-reported87.500
- German Test accuracy on Universal Dependencies v2.8self-reported80.500
- Italian Test accuracy on Universal Dependencies v2.8self-reported88.700
- French Test accuracy on Universal Dependencies v2.8self-reported89.700
- Spanish Test accuracy on Universal Dependencies v2.8self-reported91.800
- Russian Test accuracy on Universal Dependencies v2.8self-reported88.600
- Swedish Test accuracy on Universal Dependencies v2.8self-reported87.700