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
Download pytorch_model.bin from oliverguhr/fullstop-punctuation-multilingual-base: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/oliverguhr/fullstop-punctuation-multilingual-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://oliverguhr/fullstop-punctuation-multilingual-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/oliverguhr/fullstop-punctuation-multilingual-base/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- f4d8349f33754d359283791c8e381e24a9ddaba17b8c50bf7579dcd140101dc4
- Size of remote file:
- 1.11 GB
- SHA256:
- 70af5cf8c36eee1a0dfb8bafc3b6516afcbb9b8ad06be34c76ae15f68e6d5c72
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