Token Classification
Transformers
PyTorch
Safetensors
Korean
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-ko 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-ko 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-ko")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ko") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ko") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 366cfa4868d397d09f31844e0205909ca27c444f2dd630021105d1905f4e6297
- Size of remote file:
- 1.11 GB
- SHA256:
- e23077260871f686289465c07bca664824c1fe696598755a2d7c9385dedb4c44
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