Token Classification
Transformers
PyTorch
Safetensors
Japanese
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-ja 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-ja 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-ja")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ja") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ja") - Notebooks
- Google Colab
- Kaggle
| udpos -tt=token-classification -tn=udpos28 -mi=xlm-roberta-base -mt=ft --learning_rate=5e-5 --eval_steps=1000 --eval_batch_size=10 --train_batch_size=10 --multi --max_steps=1000 |