knowledge-base-embedding-model download | Hugging Bay

Q: What is this model?

saidsef/knowledge-base-embedding-model is an embedding model for sentence similarity and retrieval published by saidsef, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for sentence similarity: saidsef/knowledge-base-embedding-model · sentence transformers

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 8 listed files; full count and size unknown. Full repo upstream: 91.8 MB. The recommended download, model.safetensors, is 90.9 MB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 91.8 MB. The original release is at https://huggingface.co/saidsef/knowledge-base-embedding-model.

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-saidsef-knowledge-base-embedding-model/safety on Hugging Bay.

Q: Will saidsef/knowledge-base-embedding-model 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.

saidsef

knowledge-base-embedding-model

saidsef/knowledge-base-embedding-model

Model details

sentence similarity and retrievalLicense: apache-2.0SHA-256 recorded for 8 of 12 filessafetensors

Content controls: not assessed.

Listed for comparing sentences; for developers building text similarity tools. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

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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-saidsef-knowledge-base-embedding-model/model.safetensors",
    "model.safetensors",
)

Other ways

Run it on your computerOpen in Hugging Bay appSend to a computerApp links support one GGUF file.

8 hosted of 12 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors91 MB · 1 file Download · 91 MB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

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

8 hosted of 12 listed

Hashes recorded. Date not recorded.

File	Taille	SHA-256	Source	Télécharger
Weights
`model.safetensors` Default	90.9 MB	`1377e9af…944057ac` `1377e9af0ca0b016a9f2aa584d6fc71ab3ea6804fae21ef9fb1416e2944057ac`	Hosted	Télécharger
Tokenizer & config
`1_Pooling/config.json`	312 bytes	—	Upstream only	File metadata
`config.json`	611 bytes	`7f0faf76…61a6f760` `7f0faf76d12c68326d6296638406f9c4507fa327e52ed6fdc3678c0761a6f760`	Hosted	Télécharger
`config_sentence_transformers.json`	277 bytes	`e18d0aa6…41038147` `e18d0aa605d36947bd3cc4155853d95f2e0c6e939adb9fcd0ff1b6be41038147`	Hosted	Télécharger
`sentence_bert_config.json`	57 bytes	`2713ef71…86bc0c6c` `2713ef7170fbb7e99cf4c4d8a1685642b0454b35f606b0f3b3b23b2786bc0c6c`	Hosted	Télécharger
`special_tokens_map.json`	695 bytes	`5d5b662e…a350503a` `5d5b662e421ea9fac075174bb0688ee0d9431699900b90662acd44b2a350503a`	Hosted	Télécharger
`tokenizer.json`	711.6 KB	`851ca671…4d372c3c` `851ca67100d372ca3ae031a6abd168f53489eebfd7d89523f35c5c9b4d372c3c`	Hosted	Télécharger
`tokenizer_config.json`	1.46 KB	`c128cee6…017ca1f0` `c128cee663cc23ce44cf253471d2a538d1623c7c3ead5e465b13519a017ca1f0`	Hosted	Télécharger
`vocab.txt`	231.5 KB	—	Upstream only	File metadata
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	Télécharger
Docs
`.gitattributes`	1.52 KB	—	Upstream only	File metadata
`README.md`	10.5 KB	—	Upstream only	File metadata

Vérifier un téléchargement

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.

knowledge-base-embedding-model

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