Instructions to use JHL328/203_HW2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JHL328/203_HW2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="JHL328/203_HW2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("JHL328/203_HW2") model = AutoModelForQuestionAnswering.from_pretrained("JHL328/203_HW2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c33a967f14f8a9a6720dfd75c2a3423d46bb7536e9cd80f5118189bb7a37629
- Size of remote file:
- 496 MB
- SHA256:
- 3453eef729ef5c005b53a27b18f859e94fa4ac5422ddf5544afcabdc939ba105
路
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