sciembed-base safetensors download (596 MB) | Hugging Bay

Q: What is this model?

J0nasW/sciembed-base is an embedding model for text embeddings published by J0nasW, added to Hugging Bay Sep 25, 2026. Model from Hugging Face for feature extraction: J0nasW/sciembed-base · sentence 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: 8 files, 599.7 MB. Full repo upstream: 599.7 MB. The recommended download, model.safetensors, is 596.1 MB.

Q: What can I download here?

Hosted here: 8 files, 599.7 MB. Full repo upstream: 599.7 MB. The original release is at https://huggingface.co/J0nasW/sciembed-base.

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-j0nasw-sciembed-base/safety on Hugging Bay.

Q: Will J0nasW/sciembed-base 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.

J0nasW

sciembed-base

J0nasW/sciembed-base

text embeddingsLicense: mitHashes recordedsafetensors

Content controls: not assessed.

Model from Hugging Face for feature extraction: J0nasW/sciembed-base · sentence transformers

View as JSON →

8 hosted of 11 listed

Hashes recorded. Date not recorded.

File	大小	SHA-256	来源	下载
Weights
`model.safetensors` Default	596.1 MB	`6e146c6f…8b9a16a7` `6e146c6f40261eadde5805dae5d8ce68723d2becd1502d9c9765353b8b9a16a7`	Hosted	下载
Tokenizer & config
`1_Pooling/config.json`	312 bytes	—	Upstream only	File metadata
`config.json`	1.21 KB	`f295786e…0f70a9dd` `f295786e36cabd2023fd17bbf048f355926bb80a9ced63d22826f3310f70a9dd`	Hosted	下载
`config_sentence_transformers.json`	283 bytes	`342c797d…377fab03` `342c797d5a0300aa094bf8944d006c9f23a6851475cb26c4c2d7e06e377fab03`	Hosted	下载
`modules.json`	229 bytes	`8f4b264b…b3145460` `8f4b264b80206c830bebbdcae377e137925650a433b689343a63bdc9b3145460`	Hosted	下载
`sentence_bert_config.json`	57 bytes	`948201d8…ee986529` `948201d8329907aae938fa62f9ceeed53f5694dacc2b87b9f3b78b37ee986529`	Hosted	下载
`special_tokens_map.json`	694 bytes	`ea97ecdb…1be81049` `ea97ecdbcc73713039d8d64dbb05e3689495c96657fbd9a18f5bed381be81049`	Hosted	下载
`tokenizer.json`	3.58 MB	`3703d011…28f8f09e` `3703d011b3ee259e00b59b60772f8fd54998d79e8fa5a1388d507eff28f8f09e`	Hosted	下载
`tokenizer_config.json`	20.8 KB	`c5a7dfd4…e0ff1c17` `c5a7dfd44d93c5d7ed9b2ddcd7a6018e935ec7d53f1de45910ea9868e0ff1c17`	Hosted	下载
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	1.34 KB	—	Upstream only	File metadata

检查下载文件

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.

sciembed-base

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