Token Classification
Transformers
PyTorch
Safetensors
Russian
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-ru 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-ru 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-ru")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ru") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1663e66dd33166729ff4c9fd7ef5026f4d33909c9caaceeaaa5286cae023e872
- Size of remote file:
- 1.11 GB
- SHA256:
- 2a938ba9f7492a104fe65cd8adb4448a02b321d87320244a2e383e1b5fb99946
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