Instructions to use syssec-utd/py315-pylingual-v3-segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py315-pylingual-v3-segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="syssec-utd/py315-pylingual-v3-segmenter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py315-pylingual-v3-segmenter") model = AutoModelForTokenClassification.from_pretrained("syssec-utd/py315-pylingual-v3-segmenter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7be8f86fd18d049a582c0a667789336d4b5a565f846aebc8f26393f448066f65
- Size of remote file:
- 5.27 kB
- SHA256:
- 49944c156926886f17d6237adb9a9920d101eb4a30f9270ba0eb89cc9ac6ab1e
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