multiling-e5-large-instruct-claim-matching download

Q: What is this model?

Sami92/multiling-e5-large-instruct-claim-matching is an embedding model for sentence similarity and retrieval published by Sami92, added to Hugging Bay Sep 24, 2026. Model from Hugging Face for sentence similarity: Sami92/multiling-e5-large-instruct-claim-matching · sentence transformers

Q: Can I use it commercially?

It is released under the cc-by-4.0 license. Commercial use is allowed. cc-by-4.0: commercial use, modification, and redistribution are permitted with attribution.

Q: How big is it?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 2.27 GB. The recommended download, model.safetensors, is 2.24 GB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 2.27 GB. The original release is at https://huggingface.co/Sami92/multiling-e5-large-instruct-claim-matching.

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-sami92-multiling-e5-large-instruct-claim-matching/safety on Hugging Bay.

Q: Will Sami92/multiling-e5-large-instruct-claim-matching 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.

Sami92

multiling-e5-large-instruct-claim-matching

Sami92/multiling-e5-large-instruct-claim-matching

Model details

sentence similarity and retrievalLicense: cc-by-4.0SHA-256 recorded for 9 of 17 filessafetensors

Content controls: not assessed.

Listed for comparing sentences; for developers building text similarity tools. 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-sami92-multiling-e5-large-instruct-claim-matching/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 17 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors2.24 GB · 1 file Download · 2.24 GB (1 file)

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Check your download →

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

8 hosted of 17 listed

Hashes recorded. Date not recorded.

File	Tamaño	SHA-256	Origen	Descargar
Weights
`model.safetensors` Default	2.24 GB	`1b85bfda…f3f8dcf5` `1b85bfdaab35ad38229360b71def00796e06e6eb06e6cb4e6991cb0bf3f8dcf5`	Hosted	Descargar
Tokenizer & config
`1_Pooling/config.json`	297 bytes	—	Upstream only	File metadata
`config.json`	781 bytes	`e7faa4a6…1ad0b506` `e7faa4a60201ea54c071e88176db2b870615075aaf3c261c157fee281ad0b506`	Hosted	Descargar
`config_sentence_transformers.json`	202 bytes	`ac250d5c…a10ab206` `ac250d5c56b016139ae84fb3b8233c84be936b0823a87de50d267fcea10ab206`	Hosted	Descargar
`prompt.txt`	1.85 KB	—	Upstream only	File metadata
`sentence_bert_config.json`	53 bytes	`ec8e29d6…5a8d0d51` `ec8e29d6dcb61b611b7d3fdd2982c4524e6ad985959fa7194eacfb655a8d0d51`	Hosted	Descargar
`sentencepiece.bpe.model`	5.07 MB	`cfc8146a…e0db2865` `cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865`	Upstream only	File metadata
`similarity_histogram.html`	3.99 MB	—	Upstream only	File metadata
`similarity_histogram.jpg`	124.3 KB	—	Upstream only	File metadata
`special_tokens_map.json`	964 bytes	`8c785abe…4cf10835` `8c785abebea9ae3257b61681b4e6fd8365ceafde980c21970d001e834cf10835`	Hosted	Descargar
`threshold_scores.html`	3.65 MB	—	Upstream only	File metadata
`threshold_scores.jpg`	206.5 KB	—	Upstream only	File metadata
`tokenizer.json`	17.1 MB	`883b0371…0f137085` `883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085`	Hosted	Descargar
`tokenizer_config.json`	1.37 KB	`e1ec6756…1aa94cbb` `e1ec67565784371f31907e67504fc5113b1a67df0bd57add501d20cc1aa94cbb`	Hosted	Descargar
`modules.json`	349 bytes	`84e40c8e…7cf642cf` `84e40c8e006c9b1d6c122e02cba9b02458120b5fb0c87b746c41e0207cf642cf`	Hosted	Descargar
Docs
`.gitattributes`	1.57 KB	—	Upstream only	File metadata
`README.md`	49.9 KB	—	Upstream only	File metadata

Comprobar una descarga

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.

multiling-e5-large-instruct-claim-matching

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