bert-large-mrqa safetensors download (1.34 GB) | Hugging Bay

Q: What is this model?

VMware/bert-large-mrqa is an open-weights language model for question answering published by VMware, added to Hugging Bay Sep 25, 2026. Model from Hugging Face for question answering: VMware/bert-large-mrqa · 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 5 listed files; full count and size unknown. Full repo upstream: 2.67 GB. The recommended download, model.safetensors, is 1.34 GB.

Q: What can I download here?

Hosted here: at least 5 listed files; full count and size unknown. Full repo upstream: 2.67 GB. The original release is at https://huggingface.co/VMware/bert-large-mrqa.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 5 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-vmware-bert-large-mrqa/safety on Hugging Bay.

Q: Will VMware/bert-large-mrqa 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.

VMware

bert-large-mrqa

VMware/bert-large-mrqa

question answeringLicense: apache-2.0Hashes recordedsafetensors · BIN

Content controls: not assessed.

Model from Hugging Face for question answering: VMware/bert-large-mrqa · transformers

View as JSON →

5 hosted of 9 listed

Hashes recorded. Date not recorded.

File	Size	SHA-256	Source	Download
Weights
`model.safetensors` Default	1.34 GB	`34a3a2a1…e86e71dc` `34a3a2a169d7df33940698aeaf11e3ebf2ab23a4b529756ba5426941e86e71dc`	Hosted	Download
`pytorch_model.bin`	1.34 GB	`896c3745…6684b291` `896c37453d889eb2e23334563b0ac63c59a7e6530be83852b6bda4776684b291`	Upstream only	File metadata
Tokenizer & config
`config.json`	675 bytes	`6c3ed184…43fb352c` `6c3ed1846de1adcd5cef28111f43c3d219cb3fc34365c7bb1f3deecd43fb352c`	Hosted	Download
`tokenizer_config.json`	349 bytes	`80c10781…bfeac299` `80c107818a1f64b29fc85e2d26e375ed7e10986a46b9cab5b77a459ebfeac299`	Hosted	Download
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`special_tokens_map.json`	125 bytes	`b6d346be…76237ee3` `b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3`	Hosted	Download
`tokenizer.json`	711.4 KB	`d241a60d…1f9e5c66` `d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66`	Hosted	Download
Docs
`.gitattributes`	1.48 KB	—	Upstream only	File metadata
`README.md`	6.22 KB	—	Upstream only	File metadata

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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-mrqa

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