Instructions to use Nadav/MacSQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nadav/MacSQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Nadav/MacSQuAD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Nadav/MacSQuAD") model = AutoModelForQuestionAnswering.from_pretrained("Nadav/MacSQuAD") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2ea3dc9d49b76b06b999b33ce9572f7517b163ca267360459be08550a198f81
- Size of remote file:
- 434 MB
- SHA256:
- decc6a56af37743abcf702b7d62cafa22fde6b0a7ee269c98fa222d0e147770b
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