Instructions to use madlag/bert-large-uncased-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madlag/bert-large-uncased-squadv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="madlag/bert-large-uncased-squadv2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("madlag/bert-large-uncased-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("madlag/bert-large-uncased-squadv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e341d365fab199be5c616d253629bcb992ac8ffc6581b885af14e9a9ddb4fa16
- Size of remote file:
- 1.34 GB
- SHA256:
- 26e2f68cb08bcd1de7cfebbbac53f48edecf920fdf6ebae59d341a0f792d775b
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