Instructions to use vuiseng9/bert-base-squadv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuiseng9/bert-base-squadv1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vuiseng9/bert-base-squadv1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vuiseng9/bert-base-squadv1") model = AutoModelForQuestionAnswering.from_pretrained("vuiseng9/bert-base-squadv1") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
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- model.safetensors +3 -0
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eval_nbest_predictions.json filter=lfs diff=lfs merge=lfs -text
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bert-base-squadv1.onnx filter=lfs diff=lfs merge=lfs -text
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version https://git-lfs.github.com/spec/v1
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oid sha256:ae94201b924ff359fcb2e3ca49ddac2bfd76ed851d1a584ed60a3c5d68d07fe7
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size 437958648
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