Instructions to use deepset/gelectra-large-germanquad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gelectra-large-germanquad 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-large-germanquad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/gelectra-large-germanquad") model = AutoModelForQuestionAnswering.from_pretrained("deepset/gelectra-large-germanquad", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepset/gelectra-large-germanquad: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/deepset/gelectra-large-germanquad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepset/gelectra-large-germanquad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepset/gelectra-large-germanquad/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- b056e70860d8a10a463f10a64efc3e03e450807173d5bbb4ec64b4b2ee5fdc67
- Size of remote file:
- 1.34 GB
- SHA256:
- 5b789819b395c91e702a0ce590bcb87978f16ca73213254db825171125b5a227
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