Token Classification
Transformers
PyTorch
Safetensors
Vietnamese
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-vi 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-vi 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-vi")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-vi") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-vi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdefea8fae035a75c8a2f35027305dc68e6478c44f8bb5efff3ef0a1884603df
- Size of remote file:
- 1.11 GB
- SHA256:
- 62877d72bb7effc3d3b8d5c856dfa4a75194f9f03d0f3bd3d5b1797578e4803f
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