TuPy-Bert-Large-Binary-Classifier download | Hugging Bay

Q: What is this model?

Silly-Machine/TuPy-Bert-Large-Binary-Classifier is an open-weights language model for text classification published by Silly-Machine, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for text classification: Silly-Machine/TuPy-Bert-Large-Binary-Classifier · transformers

Q: Can I use it commercially?

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

Q: How big is it?

Hosted here: at least 7 listed files; full count and size unknown. Full repo upstream: 1.34 GB. The recommended download, model.safetensors, is 1.34 GB.

Q: What can I download here?

Hosted here: at least 7 listed files; full count and size unknown. Full repo upstream: 1.34 GB. The original release is at https://huggingface.co/Silly-Machine/TuPy-Bert-Large-Binary-Classifier.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 7 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-silly-machine-tupy-bert-large-binary-classifier/safety on Hugging Bay.

Q: Will Silly-Machine/TuPy-Bert-Large-Binary-Classifier 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.

Silly-Machine

TuPy-Bert-Large-Binary-Classifier

Can I run TuPy-Bert-Large-Binary-Classifier on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 7 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.

TuPy-Bert-Large-Binary-Classifier is an open-weights language model for text classification. 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: 8bc5c017ce5cc0c3d0e6b0eac271a0a73c45322ef0a5a547ee9f3cb247cfe5c5. 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 2026-09-03 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

Silly-Machine/TuPy-Bert-Large-Binary-Classifier

Model details

text classificationLicense: mitSHA-256 recorded for 7 of 8 filessafetensors

Content controls: not assessed.

The catalog lists this model for sorting text into categories. Catalog sources: license, cardMetadata.license, baseModel, task, cardMetadata.pipelineTag.

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    "model.safetensors",
)

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

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

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