stsb-roberta-large safetensors download | Hugging Bay

Q: What is this model?

cross-encoder/stsb-roberta-large is an open-weights language model for text ranking published by cross-encoder, added to Hugging Bay Jun 29, 2026. Model from Hugging Face for text ranking: cross-encoder/stsb-roberta-large · sentence 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 8 listed files; full count and size unknown. Full repo upstream: 13.9 GB. The recommended download, model.safetensors, is 1.42 GB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 13.9 GB. The original release is at https://huggingface.co/cross-encoder/stsb-roberta-large.

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-cross-encoder-stsb-roberta-large/safety on Hugging Bay.

Q: Will cross-encoder/stsb-roberta-large 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.

cross-encoder

stsb-roberta-large

Can I run stsb-roberta-large on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 8 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.

stsb-roberta-large is an open-weights language model for text ranking. 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: 613af2a4f0a5deb23b07c71649c50a1e1cd76470697f3104e11a7859d80ca832. 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 2025-04-15 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

cross-encoder/stsb-roberta-large

Model details

text rankingLicense: apache-2.0SHA-256 recorded for 22 of 25 filessafetensors · ONNX · BIN

Content controls: not assessed.

The catalog lists this model for ranking text against a query. Catalog sources: license, cardMetadata.license, baseModel, tags, 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-cross-encoder-stsb-roberta-large/model.safetensors",
    "model.safetensors",
)

Other ways

This file cannot open in the app. The app supports one GGUF file at a time.

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8 hosted of 25 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors1.42 GB · 1 file Download · 1.42 GB (1 file)

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Check your download →

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

8 hosted of 25 listed

Hashes recorded. Date not recorded.

File	Größe	SHA-256	Quelle	Herunterladen
Weights
`model.safetensors` Default	1.42 GB	`613af2a4…d80ca832` `613af2a4f0a5deb23b07c71649c50a1e1cd76470697f3104e11a7859d80ca832`	Hosted	Herunterladen
`onnx/model.onnx`	1.42 GB	`b26274ff…f229d55c` `b26274ff04219b5bcc8e09202205201d0e9bfb8905d02c5cb9d2e133f229d55c`	Upstream only	File metadata
`onnx/model_O1.onnx`	1.42 GB	`bee5a8a1…70352e5e` `bee5a8a1f6db9ffb7fb0a5d8fe0c0cb8c4e204dfa907a0beac4884dc70352e5e`	Upstream only	File metadata
`onnx/model_O2.onnx`	1.42 GB	`2cdde759…c96dee43` `2cdde759506204305ec7426647f8c2b16d6c7ea5ecfe23b1f573a634c96dee43`	Upstream only	File metadata
`onnx/model_O3.onnx`	1.42 GB	`40fd080a…9d5d27e2` `40fd080a90f9195f94c8b89b1a83798ae5972408acb447b159931f099d5d27e2`	Upstream only	File metadata
`onnx/model_O4.onnx`	711.1 MB	`7a03bde9…530e68f4` `7a03bde92ae3ec5d11fd57e9f63210d7f1e198800b55ea6e5b39ad93530e68f4`	Upstream only	File metadata
`onnx/model_qint8_arm64.onnx`	358.6 MB	`e015610e…6073d9d7` `e015610e2454296350ebd3b1c0e12646075066734c628c40648fc6796073d9d7`	Upstream only	File metadata
`onnx/model_qint8_avx512.onnx`	358.6 MB	`e015610e…6073d9d7` `e015610e2454296350ebd3b1c0e12646075066734c628c40648fc6796073d9d7`	Upstream only	File metadata
`onnx/model_qint8_avx512_vnni.onnx`	358.6 MB	`e015610e…6073d9d7` `e015610e2454296350ebd3b1c0e12646075066734c628c40648fc6796073d9d7`	Upstream only	File metadata
`onnx/model_quint8_avx2.onnx`	358.6 MB	`c1daad07…1c42a843` `c1daad07e40faf372bbca890456a5b4b1a3422223aaea916c4c2ba961c42a843`	Upstream only	File metadata
`openvino/openvino_model.bin`	1.42 GB	`caeed3fd…b9e5d11d` `caeed3fdd51245b064b8ba14dfa5582083b5379ddb1b4d47568e34dbb9e5d11d`	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.bin`	358.1 MB	`9592b5ae…8f4f7dc6` `9592b5aeb6e340c9648e0769a84cd2a0d9d4c60dc5b9646fa500b5838f4f7dc6`	Upstream only	File metadata
`pytorch_model.bin`	1.42 GB	`03023f7d…5fa7fd25` `03023f7dcd714c15ff27d534432a80d3bff78c9b50778a44b10585ef5fa7fd25`	Upstream only	File metadata
Tokenizer & config
`CECorrelationEvaluator_sts-dev_results.csv`	223 bytes	—	Upstream only	File metadata
`config.json`	629 bytes	`bbb09aa2…af6dfc9d` `bbb09aa22781fd4b7357c209b93266022fadba91af9e81ba7820f6a5af6dfc9d`	Hosted	Herunterladen
`flax_model.msgpack`	1.42 GB	`646b3fe4…104c11ef` `646b3fe48a91743c4bfd18fa129ee4278573dd6634324e64baa6d8b7104c11ef`	Upstream only	File metadata
`openvino/openvino_model.xml`	750.5 KB	—	Upstream only	File metadata
`openvino/openvino_model_qint8_quantized.xml`	1.38 MB	—	Upstream only	File metadata
`special_tokens_map.json`	1.01 KB	`75cbec71…13f17c1c` `75cbec71553267dadc77a583ef15122c2aa7422eb6635acc72a184a413f17c1c`	Hosted	Herunterladen
`tokenizer.json`	3.56 MB	`2bb1a22c…b6620f66` `2bb1a22cfbe25b8e5a232b7fc4d7fc5073923b45724a5f813b00811bb6620f66`	Hosted	Herunterladen
`tokenizer_config.json`	1.33 KB	`fe09e6fe…65e8b165` `fe09e6fe347cc94002f77bec394efa02db0ac151393f940d0d854af765e8b165`	Hosted	Herunterladen
`vocab.json`	798.3 KB	`ed19656e…3670ab5e` `ed19656ea1707df69134c4af35c8ceda2cc9860bf2c3495026153a133670ab5e`	Hosted	Herunterladen
`merges.txt`	456.3 KB	`1ce16647…7726adc5` `1ce1664773c50f3e0cc8842619a93edc4624525b728b188a9e0be33b7726adc5`	Hosted	Herunterladen
Docs
`.gitattributes`	445 bytes	`98c91dab…927304c1` `98c91dabef4df27b2fc7c8454d220aba47476f73593cbd034d961fe4927304c1`	Upstream only	File metadata
`README.md`	1.14 KB	`be18933a…7b10ba6d` `be18933aed9576e9820e675a4b5272bd1a0fa4bf62468c187a2ac0b77b10ba6d`	Hosted	Herunterladen

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In the folder where you saved model.safetensors, run:

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shasum -a 256 -- 'model.safetensors'

Linux

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Windows

Get-FileHash -LiteralPath 'model.safetensors' -Algorithm SHA256

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stsb-roberta-large

Can I run stsb-roberta-large on my computer?

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