Instructions to use harveyagraphcore/bert-base-uncased-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harveyagraphcore/bert-base-uncased-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="harveyagraphcore/bert-base-uncased-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("harveyagraphcore/bert-base-uncased-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("harveyagraphcore/bert-base-uncased-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harveyagraphcore/bert-base-uncased-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 218 MB
-
https://huggingface.co/harveyagraphcore/bert-base-uncased-finetuned-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harveyagraphcore/bert-base-uncased-finetuned-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harveyagraphcore/bert-base-uncased-finetuned-squad/resolve/main/pytorch_model.bin
218 MB
- Xet hash:
- ec17af8a7045c32f29631e3df01cd3bb0d4ce1e64902c6ad5cad463fdddb923c
- Size of remote file:
- 218 MB
- SHA256:
- c6ab948b84a64eb79694c0b22c4a6c49c7259d9b4d87cdbe75543868411ff5a5
路
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