gbert-base safetensors download (442 MB) | Hugging Bay

Q: What is this model?

deepset/gbert-base is an open-weights language model for masked-language modeling published by deepset, added to Hugging Bay Jun 29, 2026. Model from Hugging Face: deepset/gbert-base

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: 8 files, 1.42 GB. Full repo upstream: 1.42 GB. The recommended download, model.safetensors, is 442.2 MB.

Q: What can I download here?

Hosted here: 8 files, 1.42 GB. Full repo upstream: 1.42 GB. The original release is at https://huggingface.co/deepset/gbert-base.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 8 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-deepset-gbert-base/safety on Hugging Bay.

Q: Will deepset/gbert-base 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

deepset/gbert-base

masked-language modelingLicense: mitHashes recordedsafetensors · BIN · H5

Content controls: not assessed.

Model from Hugging Face: deepset/gbert-base

View as JSON →

8 hosted of 9 listed

Hashes recorded. Date not recorded.

File	Taille	SHA-256	Source	Télécharger
Weights
`model.safetensors` Default	442.2 MB	`1c3d31aa…132461fe` `1c3d31aa6461261d8714a4ba57033437dba5afce502be1faaa068368132461fe`	Hosted	Télécharger
`pytorch_model.bin`	442.3 MB	`227220a5…cea87867` `227220a5807266f9120d332a562ad169c95411c54ee0f2439955fe5dcea87867`	Hosted	Télécharger
Tokenizer & config
`config.json`	362 bytes	`477098ee…736a1bbf` `477098ee1a14b26b8d196d8fb8aebfb5fdf1b44bdff3286a4e10abf8736a1bbf`	Hosted	Télécharger
`tf_model.h5`	539.6 MB	`44ec20c0…f9da340a` `44ec20c0fb507650d3f8f7d68550535696945d5fed0d8dc781079a67f9da340a`	Hosted	Télécharger
`tokenizer_config.json`	83 bytes	`25f2b98f…dca10378` `25f2b98fcd4c39add7f30fa8e129c9d7d1fc2d27a37d615ae6452a47dca10378`	Hosted	Télécharger
`vocab.txt`	239.8 KB	`bf186f7f…f13039a3` `bf186f7fb9cb667d811c08b45ce64d1e8e262277bb053f05d1aef3e9f13039a3`	Hosted	Télécharger
Docs
`.gitattributes`	399 bytes	`a368a21c…5ccc6f59` `a368a21c472071feec9095f63d5f7d3433826c1c476a6995f072b6755ccc6f59`	Upstream only	File metadata
`README.md`	2.9 KB	`7dd66a45…92548118` `7dd66a45e488b471e8808c51b1101b7aa45bab54151b7eb265cfd56992548118`	Hosted	Télécharger
`license.txt`	1.05 KB	`3e6d3306…7a1cd5a2` `3e6d33063d766183a848ffabfabf0ec945be81bd9f7eb230d0f69ceb7a1cd5a2`	Hosted	Télécharger

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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

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