bert-base-mrqa safetensors download (436 MB) | Hugging Bay

Q: What is this model?

VMware/bert-base-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-base-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: 872.2 MB. The recommended download, model.safetensors, is 435.6 MB.

Q: What can I download here?

Hosted here: at least 5 listed files; full count and size unknown. Full repo upstream: 872.2 MB. The original release is at https://huggingface.co/VMware/bert-base-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-base-mrqa/safety on Hugging Bay.

Q: Will VMware/bert-base-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-base-mrqa

Can I run bert-base-mrqa on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 5 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.

bert-base-mrqa is an open-weights language model for question answering. Model weights are learned parameters; a runtime loads them. Hosting files does not prove that a complete model runs.

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

Recorded SHA-256 for model.safetensors: b345deb71a698345a022cb5931dbd60167aa521c461394e0c72fac7f0449e757. File evidence · Machine-readable facts · Run evidence and any recorded receipts · Proof Lab measurement policy. A checksum verifies byte identity, not safety; this answer asserts no measured runtime result.

Updated 2026-10-21 · Page text: As of 2026-10-21 · Content revision

Model metadata: As of 2026-09-25 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

VMware/bert-base-mrqa

Model details

question answeringLicense: apache-2.0SHA-256 recorded for 6 of 9 filessafetensors · BIN

Content controls: not assessed.

The catalog lists this model for answering questions. 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-vmware-bert-base-mrqa/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

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

Selected downloadmodel.safetensors436 MB · 1 file Download · 436 MB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

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

5 hosted of 9 listed

Hashes recorded. Date not recorded.

File	Taille	SHA-256	Source	Télécharger
Weights
`model.safetensors` Default	435.6 MB	`b345deb7…0449e757` `b345deb71a698345a022cb5931dbd60167aa521c461394e0c72fac7f0449e757`	Hosted	Télécharger
`pytorch_model.bin`	435.6 MB	`9b442a07…e8319357` `9b442a079d88b5c02cea285bc50469d13bfdb1d0cfa39a7e33c1301ee8319357`	Upstream only	File metadata
Tokenizer & config
`config.json`	673 bytes	`6377bf79…48e07380` `6377bf7912f5f85d9bf2da779741a43675298241e755bb4c0c2f7e8248e07380`	Hosted	Télécharger
`tokenizer_config.json`	348 bytes	`aa1682f6…8a20d63e` `aa1682f605197045027e3b9892312d5f6e75957b736a66af5393a18a8a20d63e`	Hosted	Télécharger
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`special_tokens_map.json`	125 bytes	`b6d346be…76237ee3` `b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3`	Hosted	Télécharger
`tokenizer.json`	711.4 KB	`d241a60d…1f9e5c66` `d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66`	Hosted	Télécharger
Docs
`.gitattributes`	1.48 KB	—	Upstream only	File metadata
`README.md`	6.22 KB	—	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.

bert-base-mrqa

Can I run bert-base-mrqa on my computer?

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