bert-large-uncased safetensors download | Hugging Bay

Q: What is this model?

google-bert/bert-large-uncased is an open-weights language model for masked-language modeling published by google-bert, added to Hugging Bay Jun 29, 2026. Model from Hugging Face for fill mask: google-bert/bert-large-uncased · transformers

Q: Can I use it commercially?

It is released under the apache-2.0 license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.

Q: How big is it?

Hosted here: at least 11 listed files; full count and size unknown. Full repo upstream: 8.22 GB. The recommended download, model.safetensors, is 1.34 GB.

Q: What can I download here?

Hosted here: at least 11 listed files; full count and size unknown. Full repo upstream: 8.22 GB. The original release is at https://huggingface.co/google-bert/bert-large-uncased.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 11 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-google-bert-bert-large-uncased/safety on Hugging Bay.

Q: Will google-bert/bert-large-uncased 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.

google-bert

bert-large-uncased

google-bert/bert-large-uncased

Model details

masked-language modelingLicense: apache-2.0Hashes recordedsafetensors · BIN · H5

Content controls: not assessed.

Listed for predicting masked text; for developers working with masked language models. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

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-google-bert-bert-large-uncased/model.safetensors",
    "model.safetensors",
)

Other ways

Run it on your computerOpen in Hugging Bay appSend to a computerApp links support one GGUF file.

11 hosted of 12 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors1.34 GB · 1 file Download · 1.34 GB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

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

11 hosted of 12 listed

Hashes recorded. Date not recorded.

File	Größe	SHA-256	Quelle	Herunterladen
Weights
`model.safetensors` Default	1.34 GB	`28349a5c…f444a4c4` `28349a5c57338d0dca591bb95de533897c2399095c69d61e09604b46f444a4c4`	Hosted	Herunterladen
`pytorch_model.bin`	1.34 GB	`be24b235…fad35434` `be24b235c46198938ac269939f8341ae479f2150dc278a342a3ffddefad35434`	Hosted	Herunterladen
Tokenizer & config
`config.json`	571 bytes	`f9a38c9f…7bff7871` `f9a38c9ffa369f288c3206d0bf352dfed11d7bdee93aa5b2de6d7fdc7bff7871`	Hosted	Herunterladen
`flax_model.msgpack`	1.34 GB	`95d8c422…807e2b64` `95d8c4223c803edaae6af2a45ef01e28c715613550ce64f82e182692807e2b64`	Hosted	Herunterladen
`rust_model.ot`	1.47 GB	`9db92b28…c1807d7f` `9db92b28d6fb0e5ab770b24ed27bde941d1f314a3c5e8c28d698025cc1807d7f`	Hosted	Herunterladen
`tf_model.h5`	1.47 GB	`81bd4971…9ce2c091` `81bd49719384ac79499b77071edcf7eaa0c0002b018b7bf435687f6b9ce2c091`	Hosted	Herunterladen
`tokenizer.json`	466.1 KB	`ce64fce7…0d8c9a98` `ce64fce797c24f68df90b40a3f74f579b336a493db14bd583fd520ea0d8c9a98`	Hosted	Herunterladen
`tokenizer_config.json`	48 bytes	`a025160e…d214bf10` `a025160ef0431f1a392f6f050c1310f4c5d9fb6f275932dbccba73c4d214bf10`	Hosted	Herunterladen
`vocab.txt`	231.5 KB	`07eced37…992038a3` `07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3`	Hosted	Herunterladen
`whole-word-masking.tar.gz`	1.25 GB	`9169b12d…e332b6ff` `9169b12daff1cc053a8b7ae4058c39eb3a2c15e5d8be160cfa8267aee332b6ff`	Hosted	Herunterladen
Docs
`.gitattributes`	491 bytes	`f0edc261…9c268240` `f0edc261ebfcf55ab4408dc897b0ab873161871b341ed1e3a27761a69c268240`	Upstream only	File metadata
`README.md`	8.96 KB	`5144d589…45be35a3` `5144d58998302e0a2830cca2c658b7a935c99e2e75bb45acf1309e6745be35a3`	Hosted	Herunterladen

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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.

bert-large-uncased

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