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
- Xet hash:
- 28883620e62f7427b9ed6da0ead3fd4eccb666a713bde132483d29bc67a61011
- Size of remote file:
- 1.11 GB
- SHA256:
- fa9873bc6ec39b5188c763ff70cc953c39228bdf7c4e30c720b94c5ba8870be9
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