Instructions to use levshechter/tibetan_code_switching_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use levshechter/tibetan_code_switching_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="levshechter/tibetan_code_switching_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("levshechter/tibetan_code_switching_model") model = AutoModelForTokenClassification.from_pretrained("levshechter/tibetan_code_switching_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6529157b7b1c5c28dd9a90fef5a131372fac6497c9285390033e86dc6ecb323
- Size of remote file:
- 5.24 kB
- SHA256:
- 6c9fb47a1b9e042c718e44870c1361222e36ba6ae11efa25444ce8a3e43c2492
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