Instructions to use intanm/mdeberta-squad2-webis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mdeberta-squad2-webis 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="intanm/mdeberta-squad2-webis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mdeberta-squad2-webis") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mdeberta-squad2-webis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from intanm/mdeberta-squad2-webis: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/intanm/mdeberta-squad2-webis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://intanm/mdeberta-squad2-webis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/intanm/mdeberta-squad2-webis/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- f61430e81037cc86787b1b61e13a5d8ea2d575aa70fb669a18fda88407539d3a
- Size of remote file:
- 1.11 GB
- SHA256:
- 46d841216b161ea0b87f25604535b7abd9dd757c4a33d6f590dcbce280aba172
路
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