bert-tiny-sst2 safetensors download (18 MB) | Hugging Bay

Q: What is this model?

takedarn/bert-tiny-sst2 is an open-weights language model for text classification published by takedarn, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for text classification: takedarn/bert-tiny-sst2 · unknown

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: 18.5 MB. The recommended download, model.safetensors, is 17.5 MB.

Q: What can I download here?

Hosted here: at least 5 listed files; full count and size unknown. Full repo upstream: 18.5 MB. The original release is at https://huggingface.co/takedarn/bert-tiny-sst2.

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-takedarn-bert-tiny-sst2/safety on Hugging Bay.

Q: Will takedarn/bert-tiny-sst2 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.

takedarn

bert-tiny-sst2

Can I run bert-tiny-sst2 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-tiny-sst2 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: e19687f3cb3df1f3cabf5749488481da26123b69ca5c30fd405a9e0308925314. 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 2026-09-24 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

takedarn/bert-tiny-sst2

Model details

text classificationLicense: apache-2.0SHA-256 recorded for 5 of 11 filessafetensors

Content controls: not assessed.

The catalog lists this model for sorting text into categories. Catalog sources: license, cardMetadata.license, baseModel, 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-takedarn-bert-tiny-sst2/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 11 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors18 MB · 1 file Download · 18 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 11 listed

Hashes recorded. Date not recorded.

File	आकार	SHA-256	स्रोत	डाउनलोड करें
Weights
`model.safetensors` Default	17.5 MB	`e19687f3…08925314` `e19687f3cb3df1f3cabf5749488481da26123b69ca5c30fd405a9e0308925314`	Hosted	डाउनलोड करें
Tokenizer & config
`config.json`	594 bytes	`dd1718f8…2783a6ac` `dd1718f8bacd3c2c884c1ba36debb4fdd1b0ab287c55f91b9431db732783a6ac`	Hosted	डाउनलोड करें
`evaluation_validation_sst2.json`	286 bytes	—	Upstream only	File metadata
`summary.txt`	661 bytes	—	Upstream only	File metadata
`tokenizer_config.json`	1.3 KB	`e711904c…9ad38e73` `e711904cac23112776b678356ccf702cf934babaa01125f698ac43bf9ad38e73`	Hosted	डाउनलोड करें
`training_summary.txt`	244 bytes	—	Upstream only	File metadata
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`special_tokens_map.json`	125 bytes	`b6d346be…76237ee3` `b6d346be366a7d1d48332dbc9fdf3bf8960b5d879522b7799ddba59e76237ee3`	Hosted	डाउनलोड करें
`tokenizer.json`	711.4 KB	`d241a60d…1f9e5c66` `d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66`	Hosted	डाउनलोड करें
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	1.51 KB	—	Upstream only	File metadata

डाउनलोड की जाँच करें

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-tiny-sst2

Can I run bert-tiny-sst2 on my computer?

डाउनलोड की जाँच करें

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