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 tokenizer.json from intanm/mdeberta-squad2-webis: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/intanm/mdeberta-squad2-webis/resolve/main/tokenizer.json
- Command line
-
hf download hf://intanm/mdeberta-squad2-webis/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/intanm/mdeberta-squad2-webis/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 6c4bff51b0197bfdb3b9e10ed1ce2b1cab5c37da075a923fab07ace799d11443
- Size of remote file:
- 16.3 MB
- SHA256:
- 35d150d7d6d0c67e5843bd50f2e2cfc5c179c89ae43191f95360dc276de21285
路
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