Token Classification
Transformers
PyTorch
Safetensors
Urdu
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-ur 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-ur 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-ur")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ur") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ur", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86099accdf724d967abba07ed2a415a25510cb931774f0756174c351a52a6657
- Size of remote file:
- 1.11 GB
- SHA256:
- 966f8781cb8241c510c159cceec636b4654dc1d3ba285443cd762c8ce20eba71
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