Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
Russian
bert
generated-from-trainer
restore_punctuation
Instructions to use markusiko/rubert-base-punctuation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use markusiko/rubert-base-punctuation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="markusiko/rubert-base-punctuation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("markusiko/rubert-base-punctuation") model = AutoModelForTokenClassification.from_pretrained("markusiko/rubert-base-punctuation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# ruBert-base for
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The model is built upon the foundation of [ruBert-base](https://huggingface.co/ai-forever/ruBert-base) and has been fine-tuned to correctly place punctuation marks in Russian sentences (it predicts the mark after each word).
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# ruBert-base for Punctuation Correction
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The model is built upon the foundation of [ruBert-base](https://huggingface.co/ai-forever/ruBert-base) and has been fine-tuned to correctly place punctuation marks in Russian sentences (it predicts the mark after each word).
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