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", device_map="auto")# 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:
- d99ff42316b2b39d38c9a0814f22421f42035c615a9743707be8af5be165dd38
- Size of remote file:
- 709 MB
- SHA256:
- d6d9988a324edd42d32d58e1d110b0f10d35b9a7d46676ea77a974a31c854167
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