Instructions to use intanm/mbert-webis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mbert-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/mbert-webis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mbert-webis") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mbert-webis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from intanm/mbert-webis: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/intanm/mbert-webis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://intanm/mbert-webis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/intanm/mbert-webis/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- afe4ebd6400f5cac4f43d12a06264b1d817338272bcfa55b837f368c6227433b
- Size of remote file:
- 709 MB
- SHA256:
- e22a230995ae7f035bab0f5d0d074b5156db3dd01eb0263a9a87c2c4cec4cedd
路
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