Model reference · open weights

splade-cocondenser-ensembledistil

splade-cocondenser-ensembledistil is an open-weight embedding model from naver, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Licence fee required Embeddings naver 1 variants 459k downloads/mo
Request a licence + hosting quote All served models Not on the shared API today — deployed on request.

About

What splade-cocondenser-ensembledistil is

SPLADE CoCondenser EnsembleDistil SPLADE model for passage retrieval. For additional details, please visit: paper: https://arxiv.org/abs/2205.04733 code: https://github.com/naver/splade Model Details This is a SPLADE Sparse Encoder model. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. Model Description - Model Type: SPLADE Sparse Encoder - Base model: Luyu/co-condenser-marco - Maximum Sequence Length: 512 tokens (256 for evaluation reproduction) - Output Dimensionality: 30522 dimensions - Similarity Function: Dot Product Full Model Architecture Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Citation If you use our checkpoint, please cite our work:

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

Makernaver
TypeEmbedding models
Context512 tokens
Variants1
Runs withsentence-transformers
Released2022-05-09
Popularity459k downloads / month
Likes64
LicenceCommercial licence needed

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together — the basis of search and RAG.

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

VariantParamsPrecisionVRAMFits 16 GBWeights
splade-cocondenser-ensembledistilBF16Weights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys splade-cocondenser-ensembledistil for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (splade-cocondenser-ensembledistil below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"splade-cocondenser-ensembledistil","input":"text to embed"}'

Details

Languages, data & research

Languages

en

Trained / evaluated on

ms_marco

Tags

sentence-transformers pytorch bert splade query-expansion document-expansion bag-of-words passage-retrieval knowledge-distillation sparse-encoder sparse feature-extraction en dataset:ms_marco

Papers

Licence

Commercial licence needed

The weights are open but cc-by-nc-sa-4.0 needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗

Sources

Weights & code

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