Instructions to use stevemobs/deberta-base-finetuned-aqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevemobs/deberta-base-finetuned-aqa 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="stevemobs/deberta-base-finetuned-aqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("stevemobs/deberta-base-finetuned-aqa") model = AutoModelForQuestionAnswering.from_pretrained("stevemobs/deberta-base-finetuned-aqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stevemobs/deberta-base-finetuned-aqa: direct link, hf CLI and curl.
- Browser
- Download file 554 MB
-
https://huggingface.co/stevemobs/deberta-base-finetuned-aqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stevemobs/deberta-base-finetuned-aqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stevemobs/deberta-base-finetuned-aqa/resolve/main/pytorch_model.bin
554 MB
- Xet hash:
- e890c5854d6a5fc52bbb8d7d78d00f4259158f8b862eab31419a68572002b13b
- Size of remote file:
- 554 MB
- SHA256:
- 893e3c535b829690f16ceafb40339903db7cf75e384939fd08e1a4debf96e1e4
路
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