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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- **Fine-Tuning Source:** The model has undergone fine-tuning using a diverse dataset comprising over 20,000 paragraphs from Russian literary works.
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- **Supported Classes:** The model is designed to predict classes following specific punctuation marks:
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- **Input Format:** To achieve optimal results, input text should be provided without punctuation marks. The model does not process changes in letter case.
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- **Fine-Tuning Source:** The model has undergone fine-tuning using a diverse dataset comprising over 20,000 paragraphs from Russian literary works.
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- **Supported Classes:** The model is designed to predict classes following specific punctuation marks: '?', '.', ',', '!', ':', '...' and ' ' (class O).
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- **Input Format:** To achieve optimal results, input text should be provided without punctuation marks. The model does not process changes in letter case.
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