Instructions to use deepset/gelectra-base-germanquad-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gelectra-base-germanquad-distilled 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="deepset/gelectra-base-germanquad-distilled")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/gelectra-base-germanquad-distilled") model = AutoModelForQuestionAnswering.from_pretrained("deepset/gelectra-base-germanquad-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepset/gelectra-base-germanquad-distilled: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/deepset/gelectra-base-germanquad-distilled/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepset/gelectra-base-germanquad-distilled/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepset/gelectra-base-germanquad-distilled/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 93c71b6481616c624e8687769a5d7b2a9edf3904761c55ad65081b9f017442e7
- Size of remote file:
- 437 MB
- SHA256:
- f5c1a0be8fe054822870fa31ef75c0d0de4015b16ad2516caaa2549caf009b17
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