Instructions to use dwmit/transliterate_try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwmit/transliterate_try with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dwmit/transliterate_try")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dwmit/transliterate_try") model = AutoModelForTokenClassification.from_pretrained("dwmit/transliterate_try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 047e4b13b9776aead9e47d164dba09c71c4992c291c7cfc0f84a72fe852edf33
- Size of remote file:
- 434 MB
- SHA256:
- e7cddcb92c203b27ed02f711f029528923ea387472e1024905da6dc782cdb094
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