distilroberta-base safetensors download | Hugging Bay

distilroberta-base

distilbert/distilroberta-base

Model details

masked-language modelingLicense: apache-2.0SHA-256 recorded for 12 of 13 filessafetensors · 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-distilbert-distilroberta-base/model.safetensors",
    "model.safetensors",
)

Other ways

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

12 hosted of 13 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors331 MB · 1 file Download · 331 MB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

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

Model facts

distilroberta-base is an open-weights language model for masked-language modeling. The primary file is model.safetensors (331 MB). The primary file needs about 2 GB free for the selected file. License: Apache 2.0. The SHA-256 prefix is 2b11ca9cf3d2. The primary Download is available now. No tested run commands for this file yet. See Files for current download availability. The size and hash details refer to the selected file, not every file in the repository. The memory figure is estimated from recorded file size with working room included. The size line uses recorded file bytes when they are present in this model record.

What's included

12 hosted of 13 listed

Weights

Tokenizer & config

README, LICENSE & .gitattributes

About this model

External Hugging Face catalog record for distilbert/distilroberta-base. Task: fill mask. Library: transformers. Languages: en. Datasets referenced by card metadata: openwebtext. Popularity signal at import: 1,484,404 downloads and 179 likes.

Quick facts

Hosted here: 12 hosted of 13 listed
Full repo upstream: 1.97 GB
Recommended file: 331.1 MB · model.safetensors
Languages: en
Format: safetensors · BIN · H5
Downloads on Hugging Face: 1,812,332
File hashes: Hashes recorded. Date not recorded.

License & source

License: apache-2.0

Check the license text and keep the required notices when you use or share the files.

Upstream source →

See the JSON record →

License: Apache 2.0. Free to use and share, keep the credit notice
Task: masked-language modeling
Hosted here: 12 files, 1.97 GB
Full repo upstream: 1.97 GB
Recommended file: 331.1 MB · model.safetensors
Files: 12 hosted of 13 listed
Download: Download here (12 hosted files)
Added to Hugging Bay: Jun 29, 2026
Updated at source: Aug 28, 2026
Source: distilbert/distilroberta-base

FAQ

What is this model?

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

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.

How big is it?

Hosted here: 12 files, 1.97 GB. Full repo upstream: 1.97 GB. The recommended download, model.safetensors, is 331.1 MB.

What can I download here?

Hosted here: 12 files, 1.97 GB. Full repo upstream: 1.97 GB. The original release is at https://huggingface.co/distilbert/distilroberta-base.

File checks and computer fit

File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 12 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-distilbert-distilroberta-base/safety on Hugging Bay.

Will distilbert/distilroberta-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.