Instructions to use RUPunct/RUPunct_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RUPunct/RUPunct_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RUPunct/RUPunct_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RUPunct/RUPunct_small") model = AutoModelForTokenClassification.from_pretrained("RUPunct/RUPunct_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RUPunct/RUPunct_small: direct link, hf CLI and curl.
- Browser
- Download file 116 MB
-
https://huggingface.co/RUPunct/RUPunct_small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RUPunct/RUPunct_small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RUPunct/RUPunct_small/resolve/main/pytorch_model.bin
116 MB
- Xet hash:
- 575abb9386b3a8ae9e616f8d0950a065ed5f779805a61310f1a6b664e2d9e357
- Size of remote file:
- 116 MB
- SHA256:
- 5b05a696d0a9629e0938852e221abcd84cc75ab67a4445fb0b4ea40630dd6b59
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