VulBERTa-MLP-Devign safetensors download | Hugging Bay

Q: What is this model?

claudios/VulBERTa-MLP-Devign is an open-weights language model for text classification published by claudios, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for text classification: claudios/VulBERTa-MLP-Devign · 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: 6 files, 501.4 MB. Full repo upstream: 501.4 MB. The recommended download, model.safetensors, is 499.4 MB.

Q: What can I download here?

Hosted here: 6 files, 501.4 MB. Full repo upstream: 501.4 MB. The original release is at https://huggingface.co/claudios/VulBERTa-MLP-Devign.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 6 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-claudios-vulberta-mlp-devign/safety on Hugging Bay.

Q: Will claudios/VulBERTa-MLP-Devign 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.

claudios

VulBERTa-MLP-Devign

claudios/VulBERTa-MLP-Devign

text classificationLicense: mitHashes recordedsafetensors

Content controls: not assessed.

Model from Hugging Face for text classification: claudios/VulBERTa-MLP-Devign · transformers

View as JSON →

6 hosted of 8 listed

Hashes recorded. Date not recorded.

File	Size	SHA-256	Source	Download
Weights
`model.safetensors` Default	499.4 MB	`cb46ffad…44f97913` `cb46ffad38b612d03741ecd4a5359c6fa7d560ebaa81baf982d17f5344f97913`	Hosted	Download
Tokenizer & config
`config.json`	733 bytes	`453262e3…46d22fe9` `453262e30b53494537fe95057d65af9bbc01b28faabc835ae60c16c346d22fe9`	Hosted	Download
`special_tokens_map.json`	27 bytes	`7271f933…f516eb05` `7271f9338f7c3c2207d3923fd47378b62cbf538fdbb36d7688d06943f516eb05`	Hosted	Download
`tokenization_vulberta.py`	1.39 KB	—	Upstream only	File metadata
`tokenizer.json`	2.05 MB	`8c50c6e6…e010efc7` `8c50c6e6b2ccaa8d406b4e02ac91da6bb4a228d3903b5e733d49f5b8e010efc7`	Hosted	Download
`tokenizer_config.json`	620 bytes	`c0c8b1a0…82ca25cb` `c0c8b1a0ee86fc43045971d6ab48183118f2e4f4e57403f9e5fdfac182ca25cb`	Hosted	Download
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	5.46 KB	`ee6f81c2…9e419f78` `ee6f81c21be61ea1f7756cdf209887dbacd9612f0f32a3cf1249e1899e419f78`	Hosted	Download

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.

VulBERTa-MLP-Devign

Check a download

Cookies