Instructions to use stevemobs/deberta-base-finetuned-squad1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevemobs/deberta-base-finetuned-squad1 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-squad1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("stevemobs/deberta-base-finetuned-squad1") model = AutoModelForQuestionAnswering.from_pretrained("stevemobs/deberta-base-finetuned-squad1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stevemobs/deberta-base-finetuned-squad1: direct link, hf CLI and curl.
- Browser
- Download file 554 MB
-
https://huggingface.co/stevemobs/deberta-base-finetuned-squad1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stevemobs/deberta-base-finetuned-squad1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stevemobs/deberta-base-finetuned-squad1/resolve/main/pytorch_model.bin
554 MB
- Xet hash:
- 4ffcea04e65b0852c0239516119ce261b34278c03852bbeda601d6bc7a42f776
- Size of remote file:
- 554 MB
- SHA256:
- bacab2166bd811ce622cf508f21f0b16ab32f4f55513c5c1613a32a737c15a4d
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.