Token Classification
Transformers
PyTorch
Belarusian
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-be 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-be 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-be")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-be") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-be", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4d75adfd1c8355c84268e3d61990202c8ea9a535effb0fa03de32b8c0fdbf2e
- Size of remote file:
- 1.11 GB
- SHA256:
- cb82bc2eca9a3c3fb3577f0a8212a4bab3808086347251ddcb7cef13a834b138
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