Instructions to use syssec-utd/py312-pylingual-v1-segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py312-pylingual-v1-segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="syssec-utd/py312-pylingual-v1-segmenter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py312-pylingual-v1-segmenter") model = AutoModelForTokenClassification.from_pretrained("syssec-utd/py312-pylingual-v1-segmenter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bd40fc479a918d33866332351d1c36cf427d9b3de91779225b046b10eb79cd4
- Size of remote file:
- 5.5 kB
- SHA256:
- 78c08b7cbd33eb522ffb739f8a56c4f32b059c46a410e82c2a3a639b393b3549
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