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 training_args.bin from dflcmu/bert-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/dflcmu/bert-finetuned-squad/resolve/main/training_args.bin
- Command line
-
hf download hf://dflcmu/bert-finetuned-squad/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dflcmu/bert-finetuned-squad/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 327f81c3569750bcb304672d1f025ecac18591f2fc12e7cd6e0bd5c0d9b75797
- Size of remote file:
- 3.58 kB
- SHA256:
- 513204c3d352cf008ad05cc6e68efcfd524508b5e505c7f87532de00155784df
路
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