Instructions to use deepset/gbert-base-germandpr-reranking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-base-germandpr-reranking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deepset/gbert-base-germandpr-reranking")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deepset/gbert-base-germandpr-reranking") model = AutoModelForSequenceClassification.from_pretrained("deepset/gbert-base-germandpr-reranking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from deepset/gbert-base-germandpr-reranking: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/deepset/gbert-base-germandpr-reranking/resolve/main/model.safetensors
- Command line
-
hf download hf://deepset/gbert-base-germandpr-reranking/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/deepset/gbert-base-germandpr-reranking/resolve/main/model.safetensors
440 MB
- Xet hash:
- d3fe1cadb040c57a9fdb692fa95eb230563f346857ea7bd3265b95e5f7875fc8
- Size of remote file:
- 440 MB
- SHA256:
- 64295d811c1319f6e45c62d248b016a7665597a4c94d9094459212c10be6b181
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