Instructions to use dflcmu/bert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dflcmu/bert-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="dflcmu/bert-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dflcmu/bert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("dflcmu/bert-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dflcmu/bert-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/dflcmu/bert-finetuned-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dflcmu/bert-finetuned-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dflcmu/bert-finetuned-squad/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- 278310a65562f42edac98a9f471897a3dffb8d0e06053fc03f886522fbbe6830
- Size of remote file:
- 496 MB
- SHA256:
- e6e01518e1c275c13570f77c6ab15ec481c54816016689edbcc2ec9721a14e4c
路
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