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

Can I run gbert-base-germandpr-reranking on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 7 hosted files; check the file list for the exact selection. A file-size fit is an estimate, not a successful run or speed promise. Read the license before use.

gbert-base-germandpr-reranking is an open-weights language model for text classification. Model weights are learned parameters; a runtime loads them. A hosted file is an individual file Hugging Bay can serve. SHA-256 identifies exact file bytes. The availability date above applies to this file record, not the upstream metadata update date.

As of the recorded file availability check on 2026-10-22.

Recorded SHA-256 for model.safetensors: 64295d811c1319f6e45c62d248b016a7665597a4c94d9094459212c10be6b181. Exact file evidence · Machine-readable source facts · Run evidence and any recorded receipts. A checksum fingerprints only the named file bytes; it does not prove safety, quality, a complete multi-file selection or a successful run.

Updated 2026-10-21 · Page-copy source revision 8871bab84e1cf80aea1686a944ec335078a6c036 · Source files: apps/web/lib/model-page-template.mjs, apps/web/lib/model-page-v3.mjs, apps/web/lib/aeo-page.mjs · Full revision record

Model metadata: As of 2026-07-31 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

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.

View as JSON →

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

This file cannot open in the app. The app supports one GGUF file at a time.

See app requirements

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

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

Open-file help

Content controls: not assessed.

Check your download →

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

7 hosted of 9 listed

Hashes recorded. Date not recorded.

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

Check a download

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

Can I run gbert-base-germandpr-reranking on my computer?

Check a download

Cookies