Instructions to use tyavika/pytorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tyavika/pytorch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="tyavika/pytorch")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("tyavika/pytorch") model = AutoModelForQuestionAnswering.from_pretrained("tyavika/pytorch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab69ba8683ed507558e7c69061311e49024478d59e7e964a6346df15128b9921
- Size of remote file:
- 267 MB
- SHA256:
- 2518ad96402875c245cb1d1be915a7898c1d670934f965f6ceafe40d9b82a470
路
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