Instructions to use hung200504/1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hung200504/1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/1") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8ce7420f11fa21550c210011dd48da37e08219e78399c01931554888d624c18
- Size of remote file:
- 431 MB
- SHA256:
- 72b0e64b3266bf68a6580f9e72e916f1ce67497fde62e9894e9195b3008692fb
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