Instructions to use syssec-utd/py315-pylingual-v2-segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py315-pylingual-v2-segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="syssec-utd/py315-pylingual-v2-segmenter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py315-pylingual-v2-segmenter") model = AutoModelForTokenClassification.from_pretrained("syssec-utd/py315-pylingual-v2-segmenter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77432174ed09e36f2a36de5aeb990e102da6b298d0c4409b379d34f22ed4d77d
- Size of remote file:
- 5.97 kB
- SHA256:
- 67fae74dda17f380a2f90d295fe2c147067473979c0911db46a1cac8beb78102
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