inclusively-classification download | Hugging Bay

Q: What is this model?

E-MIMIC/inclusively-classification is an open-weights language model for text classification published by E-MIMIC, added to Hugging Bay Sep 21, 2026. Model from Hugging Face for text classification: E-MIMIC/inclusively-classification · transformers

Q: Can I use it commercially?

It is released under the cc-by-nc-sa-4.0 license. Commercial use is not allowed. cc-by-nc-sa-4.0: commercial use is not permitted; non-commercial adaptations require attribution and share-alike.

Q: How big is it?

Hosted here: 4 files, 443.1 MB. Full repo upstream: 885.9 MB. The recommended download, model.safetensors, is 442.8 MB.

Q: What can I download here?

Hosted here: 4 files, 443.1 MB. Full repo upstream: 885.9 MB. The original release is at https://huggingface.co/E-MIMIC/inclusively-classification.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 4 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-e-mimic-inclusively-classification/safety on Hugging Bay.

Q: Will E-MIMIC/inclusively-classification 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.

E-MIMIC

inclusively-classification

Can I run inclusively-classification on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 4 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.

inclusively-classification 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: 624a5d71c4eaa9cc92080eeccd567fe05d9796b3ba2d1cf67c5ba017b8113b32. 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 2026-09-21 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

E-MIMIC/inclusively-classification

Model details

text classificationLicense: cc-by-nc-sa-4.0SHA-256 recorded for 6 of 8 filessafetensors · BIN

Content controls: not assessed.

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

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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-e-mimic-inclusively-classification/model.safetensors",
    "model.safetensors",
)

Other ways

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

Selected downloadmodel.safetensors443 MB · 1 file Download · 443 MB (1 file)

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

4 hosted of 8 listed

Hashes recorded. Date not recorded.

File	Size	SHA-256	Source	Download
Weights
`model.safetensors` Default	442.8 MB	`624a5d71…b8113b32` `624a5d71c4eaa9cc92080eeccd567fe05d9796b3ba2d1cf67c5ba017b8113b32`	Hosted	Download
`pytorch_model.bin`	442.9 MB	`f11f5dfe…dd606874` `f11f5dfedbc3c7bbb7ca7e3eea0fa23fa32cde1b349449bcbe683cf3dd606874`	Upstream only	File metadata
`training_args.bin`	2.99 KB	`bc383b56…f3496518` `bc383b5651db0bb264bad9740e8b854a6208441cb65120005c8af42af3496518`	Upstream only	File metadata
Tokenizer & config
`config.json`	890 bytes	`50f507c5…73bb67d2` `50f507c593e5744c06561356b49bf1f126ee9cff3aa5d9d13a85144d73bb67d2`	Hosted	Download
`tokenizer_config.json`	59 bytes	`4c052d60…3ec558ba` `4c052d60b505817149a29e88c5292d7779ddecfadbfc6208729e61403ec558ba`	Hosted	Download
`vocab.txt`	235.1 KB	`873dbed1…fa51e6d0` `873dbed1150738ae871995b5fc7ed04e7a3f5be0cb496c0219df4d3efa51e6d0`	Hosted	Download
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	3.03 KB	—	Upstream only	File metadata

Check a download

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.

inclusively-classification

Can I run inclusively-classification on my computer?

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