# splade-cocondenser-selfdistil by NAVER: Open-Weight Model Source: https://savrn.com/models/splade-cocondenser-selfdistil Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Model Card SPLADE model for passage retrieval. For additional details, please visit: 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. First install the Sentence Transformers library: Then you can load this model and run inference. If you use our checkpoint, please cite our work Excerpt from the card by NAVER, licensed cc-by-nc-sa-4.0. ## Configuration Architecture BertForMaskedLM Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 30,522 Stored precision float32 Model type bert ## Identity and Version Repository naver/splade-cocondenser-selfdistil Publisher NAVER Task Feature extraction Modality Text Library sentence-transformers Parameters Not stated by the source Languages en Revision 31f3eb4b5aae648cde974db3b7b44bf2d095106b First published 2022-05-09 Last updated 2025-06-30 ## Files and Weights 12 files, 438.8 MB in total. The weights are 1 file totalling 438.1 MB in bin. Weights1 file · 438.1 MB Configuration6 files · 1.5 KB Tokenizer3 files · 698.1 KB Documentation1 file · 3.9 KB Repository1 file · 1.2 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | pytorch_model.bin | Weights | 438.1 MB | 0a44b51cf90c | | 1_SpladePooling/config.json | Configuration | 106 B | — | | config.json | Configuration | 670 B | — | | config_sentence_transformers.json | Configuration | 274 B | — | | modules.json | Configuration | 274 B | — | | sentence_bert_config.json | Configuration | 57 B | — | | special_tokens_map.json | Configuration | 112 B | — | | README.md | Documentation | 3.9 KB | — | | .gitattributes | Repository | 1.2 KB | — | | tokenizer.json | Tokenizer | 466.1 KB | — | | tokenizer_config.json | Tokenizer | 466 B | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License cc-by-nc-sa-4.0 Access Open weights, no gate Download size 438.1 MB [Download from NAVER](https://huggingface.co/naver/splade-cocondenser-selfdistil) Released by NAVER through its official repository on Hugging Face. ## Built From - Described by arXiv:2205.04733 - Trained on (disclosed) ms_marco ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 438.1 MB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About splade-cocondenser-selfdistil ### Can I use splade-cocondenser-selfdistil commercially? Not without separate permission. splade-cocondenser-selfdistil is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0. CC BY-NC-SA 4.0 permits non-commercial sharing and adapting with credit, and requires adaptations to use the same license. Commercial use needs separate permission. ### What is splade-cocondenser-selfdistil's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Feature extraction ### [all-MiniLM-L6-v2](https://savrn.com/models/all-minilm-l6-v2-2) [Joshua](https://savrn.com/model-publishers/xenova) https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then use the model to compute embeddings like this: You can convert this Tensor to a nested JavaScript array using.tolist(): Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx). Open weights apache-2.0 512 tokens transformers.js [View model](https://savrn.com/models/all-minilm-l6-v2-2) Model · Feature extraction ### [bge-base-en-v1.5](https://savrn.com/models/bge-base-en-v1-5-2) [Joshua](https://savrn.com/model-publishers/xenova) https://huggingface.co/BAAI/bge-base-en-v1.5 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: You can then use the model to compute embeddings, as follows: You can also use the model for retrieval. For example: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx). Open weights mit 512 tokens transformers.js [View model](https://savrn.com/models/bge-base-en-v1-5-2) Model · Feature extraction ### [clap-htsat-unfused](https://savrn.com/models/clap-htsat-unfused) [LAION eV](https://savrn.com/model-publishers/laion) The abstract of the paper states that: You can use this model for zero shot audio classification or extracting audio and/or textual features. 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It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical… Open weights mit 512 tokens sentence-transformers [View model](https://savrn.com/models/bge-large-zh-v1-5) Model · Feature extraction ### [wavlm-large](https://savrn.com/models/wavlm-large) [Microsoft](https://savrn.com/model-publishers/microsoft) The large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin… Open weights transformers [View model](https://savrn.com/models/wavlm-large) Model · Feature extraction ### [1](https://savrn.com/models/1) [Unsloth Backup Account](https://savrn.com/model-publishers/unslothai) Open weights 2,048 tokens transformers [View model](https://savrn.com/models/1) ## NAVER [All models and datasets](https://savrn.com/model-publishers/naver) ## Versions - [31f3eb4b5aae](https://savrn.com/models/splade-cocondenser-selfdistil/versions/31f3eb4b5aae) · current 2026-10-06 ## Explore More - [All feature extraction models](https://savrn.com/models/tasks/feature-extraction) - [All models under cc-by-nc-sa-4.0](https://savrn.com/models/licenses/cc-by-nc-sa-4-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-01. - [Hugging Face record](https://huggingface.co/naver/splade-cocondenser-selfdistil) - [How the hub is built](https://savrn.com/model-hub/methodology)