Instructions to use Palak/microsoft_deberta-large_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Palak/microsoft_deberta-large_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="Palak/microsoft_deberta-large_squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Palak/microsoft_deberta-large_squad") model = AutoModelForQuestionAnswering.from_pretrained("Palak/microsoft_deberta-large_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Palak/microsoft_deberta-large_squad: direct link, hf CLI and curl.
- Browser
- Download file 1.62 GB
-
https://huggingface.co/Palak/microsoft_deberta-large_squad/resolve/main/pytorch_model.bin
- Command line
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hf download hf://Palak/microsoft_deberta-large_squad/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/Palak/microsoft_deberta-large_squad/resolve/main/pytorch_model.bin
1.62 GB
- Xet hash:
- 2918e2276e68362d7852386eaebacf184605d125c304c16fc644f6e99a1f396b
- Size of remote file:
- 1.62 GB
- SHA256:
- f0183e6a88fdc9807efac1477257af57f93535ef4d9d9cf2964ae7c277ddeb9e
路
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