gbert-base-germandpr-reranking download | Hugging Bay

Q: What is this model?

deepset/gbert-base-germandpr-reranking is an open-weights language model for text classification published by deepset, added to Hugging Bay Jun 30, 2026. Model from Hugging Face: deepset/gbert-base-germandpr-reranking

Q: Can I use it commercially?

It is released under the mit license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.

Q: How big is it?

Hosted here: at least 7 listed files; full count and size unknown. The full repository size is not recorded. The recommended download, model.safetensors, is 439.7 MB.

Q: What can I download here?

Hosted here: at least 7 listed files; full count and size unknown. Full repo upstream: unknown. The original release is at https://huggingface.co/deepset/gbert-base-germandpr-reranking.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 7 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-deepset-gbert-base-germandpr-reranking/safety on Hugging Bay.

Q: Will deepset/gbert-base-germandpr-reranking run on my computer?

We can’t check the fit; we don’t know your machine. We don’t have your machine’s specs, so no check ran and no verdict is shown. Answer two quick questions and we’ll estimate, from the memory you tell us, whether it fits your machine. Or skip it; you can still download; fit isn’t checked.

deepset

gbert-base-germandpr-reranking

deepset/gbert-base-germandpr-reranking

Model details

text classificationLicense: mitSHA-256 recorded for 7 of 9 filessafetensors · BIN

Content controls: not assessed.

The catalog lists this model for sorting text into categories. Catalog sources: license, task.

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Your next step

Use it from code

This Python snippet downloads the selected file. Running the model needs its own library and setup.

from urllib.request import urlretrieve

urlretrieve(
    "https://huggingbay.xyz/api/downloads/hf-model-deepset-gbert-base-germandpr-reranking/model.safetensors",
    "model.safetensors",
)

Other ways

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7 hosted of 9 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors440 MB · 1 file Download · 440 MB (1 file)

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Check your download →

Compare your file’s SHA-256 with the recorded hash.

7 hosted of 9 listed

Hashes recorded. Date not recorded.

File	Taille	SHA-256	Source	Télécharger
Weights
`model.safetensors` Default	439.7 MB	`64295d81…0be6b181` `64295d811c1319f6e45c62d248b016a7665597a4c94d9094459212c10be6b181`	Hosted	Télécharger
`pytorch_model.bin`	0 bytes	—	Upstream only	File metadata
Tokenizer & config
`config.json`	867 bytes	`5bf69599…17ed94b7` `5bf6959948772b5c4388304e313401e24a18ba9e917407c72906df3417ed94b7`	Hosted	Télécharger
`tokenizer.json`	479.1 KB	`dc9c3c54…1ebf4013` `dc9c3c542d09756cd098cfe0208cfe4215b9ceb3bd008863b3f685f41ebf4013`	Hosted	Télécharger
`tokenizer_config.json`	376 bytes	`22a7e720…6728aa97` `22a7e72093c82bd6fe4e7ac2a4aa4bfaa4ac928b0fe0630cdd3467e16728aa97`	Hosted	Télécharger
`vocab.txt`	239.8 KB	`bf186f7f…f13039a3` `bf186f7fb9cb667d811c08b45ce64d1e8e262277bb053f05d1aef3e9f13039a3`	Hosted	Télécharger
`special_tokens_map.json`	112 bytes	`303df45a…5ec200e3` `303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3`	Hosted	Télécharger
Docs
`README.md`	4.36 KB	`abdba805…56b43522` `abdba805efc044fdbb1e1f2902e982ea2d69939930a2e59d2878976056b43522`	Hosted	Télécharger
`.gitattributes`	0 bytes	—	Upstream only	File metadata

Vérifier un téléchargement

In the folder where you saved model.safetensors, run:

macOS

shasum -a 256 -- 'model.safetensors'

Linux

sha256sum -- 'model.safetensors'

Windows

Get-FileHash -LiteralPath 'model.safetensors' -Algorithm SHA256

Compare the result with the recorded SHA-256.

gbert-base-germandpr-reranking

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