Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
xlm-roberta
punctuation prediction
punctuation
Instructions to use oliverguhr/fullstop-punctuation-multilingual-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oliverguhr/fullstop-punctuation-multilingual-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="oliverguhr/fullstop-punctuation-multilingual-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base") model = AutoModelForTokenClassification.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update README.md
added dutch samplefe99fb0
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Adding `safetensors` variant of this model (#3)
- Adding `safetensors` variant of this model (806db730b306142c93ad972875174264d07d74e2) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>41892e7
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citation added
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added multilingual tag as suggested in #2
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Update README.md
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initial model push
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metadata added
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Create README.md
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initial commit
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