Instructions to use mrm8488/RuPERTa-base-finetuned-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/RuPERTa-base-finetuned-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mrm8488/RuPERTa-base-finetuned-pos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/RuPERTa-base-finetuned-pos") model = AutoModelForTokenClassification.from_pretrained("mrm8488/RuPERTa-base-finetuned-pos") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e7022be9e6786f8dc9455a87201ce0dd9ba281be270cd7bfce7c58a8a2525c8
- Size of remote file:
- 502 MB
- SHA256:
- 2c131abd253d68b4f30fd23792b0a3f16d87aeceb8a94cf8dfd0311ee29108af
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