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USER2-small

Name: USER2-small
Availability: InStock

USER2 is a new generation of the Universal Sentence Encoder for Russian, designed for sentence representation with long-context support of up to 8,192 tokens.

The models are built on top of the RuModernBERT encoders and are fine-tuned for retrieval and semantic tasks.
They also support Matryoshka Representation Learning (MRL) — a technique that enables reducing embedding size with minimal loss in representation quality.

This is a small model with 34 million parameters.

Model	Size	Context Length	Hidden Dim	MRL Dims
`deepvk/USER2-small`	34M	8192	384	[32, 64, 128, 256, 384]
`deepvk/USER2-base`	149M	8192	768	[32, 64, 128, 256, 384, 512, 768]

Performance

To evaluate the model, we measure quality on the MTEB-rus benchmark. Additionally, to measure long-context retrieval, we run Russian subset of MultiLongDocRetrieval (MLDR) task.

MTEB-rus

Model	Size	Hidden Dim	Context Length	MRL support	Mean(task)	Mean(taskType)	Classification	Clustering	MultiLabelClassification	PairClassification	Reranking	Retrieval	STS
`USER-base`	124M	768	512	❌	58.11	56.67	59.89	53.26	37.72	59.76	55.58	56.14	74.35
`USER-bge-m3`	359M	1024	8192	❌	62.80	62.28	61.92	53.66	36.18	65.07	68.72	73.63	76.76
`multilingual-e5-base`	278M	768	512	❌	58.34	57.24	58.25	50.27	33.65	54.98	66.24	67.14	70.16
`multilingual-e5-large-instruct`	560M	1024	512	❌	65.00	63.36	66.28	63.13	41.15	63.89	64.35	68.23	76.48
`jina-embeddings-v3`	572M	1024	8192	✅	63.45	60.93	65.24	60.90	39.24	59.22	53.86	71.99	76.04
`ru-en-RoSBERTa`	404M	1024	512	❌	61.71	60.40	62.56	56.06	38.88	60.79	63.89	66.52	74.13
`USER2-small`	34M	384	8192	✅	58.32	56.68	59.76	57.06	33.56	54.02	58.26	61.87	72.25
`USER2-base`	149M	768	8192	✅	61.12	59.59	61.67	59.22	36.61	56.39	62.06	66.90	74.28

MLDR-rus

Model	Size	nDCG@10 ↑
`USER-bge-m3`	359M	58.53
`KaLM-v1.5`	494M	53.75
`jina-embeddings-v3`	572M	49.67
`E5-mistral-7b`	7.11B	52.40
`USER2-small`	34M	51.69
`USER2-base`	149M	54.17

We compare only model with context length of 8192.

Matryoshka

To evaluate MRL capabilities, we also use MTEB-rus, applying dimensionality cropping to the embeddings to match the selected size.

Usage

Prefixes

This model is trained similarly to Nomic Embed and expects task-specific prefixes to be added to the input. The choice of prefix depends on the specific task. We follow a few general guidelines when selecting a prefix:

"classification: " is the default and most universal prefix, often performing well across a variety of tasks.
"clustering: " is recommended for clustering applications: group texts into clusters, discover shared topics, or remove semantic duplicates.
"search_query: " and "search_document: " are intended for retrieval and reranking tasks. Also, in some classification tasks, especially with shorter texts, "search_query" shows superior performance to other prefixes. On the other hand, "search_document" can be beneficial for long-context sentence similarity tasks.

However, we encourage users to experiment with different prefixes, as certain domains may benefit from specific ones.

Sentence Transformers

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("deepvk/USER2-small")

query_embeddings = model.encode(["Когда был спущен на воду первый миноносец «Спокойный»?"], prompt_name="search_query")
document_embeddings = model.encode(["Спокойный (эсминец)\nЗачислен в списки ВМФ СССР 19 августа 1952 года."], prompt_name="search_document")

similarities = model.similarity(query_embeddings, document_embeddings)

To truncate the embedding dimension, simply pass the new value to the model initialization:

model = SentenceTransformer("deepvk/USER2-small", truncate_dim=128)

This model was trained with dimensions [32, 64, 128, 256, 384], so it’s recommended to use one of these for best performance.

Transformers

python

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel


def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = (
        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    )
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
        input_mask_expanded.sum(1), min=1e-9
    )


queries = ["search_query: Когда был спущен на воду первый миноносец «Спокойный»?"]
documents = ["search_document: Спокойный (эсминец)\nЗачислен в списки ВМФ СССР 19 августа 1952 года."]

tokenizer = AutoTokenizer.from_pretrained("deepvk/USER2-small")
model = AutoModel.from_pretrained("deepvk/USER2-small")

encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    queries_outputs = model(**encoded_queries)
    documents_outputs = model(**encoded_documents)

query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)

similarities = query_embeddings @ doc_embeddings.T

To truncate the embedding dimension, select the first values:

python

query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
query_embeddings = query_embeddings[:, :truncate_dim]
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)

Training details

This is the small version with 34 million parameters, based on RuModernBERT-small.
It was fine-tuned in three stages: RetroMAE, Weakly Supervised Fine-Tuning, and Supervised Fine-Tuning.

Following the bge-m3 training strategy, we use RetroMAE as a retrieval-oriented continuous pretraining step.
Leveraging data from the final stage of RuModernBERT training, RetroMAE enhances retrieval quality—particularly for long-context inputs.

To follow best practices for building a state-of-the-art encoder, we rely on large-scale training with weakly related text pairs.
However, such datasets are not publicly available for Russian, unlike for English or Chinese.
To overcome this, we apply two complementary strategies:

Cross-lingual transfer: We train on both English and Russian data, leveraging English resources (nomic-unsupervised) alongside our in-house English-Russian parallel corpora.
Unsupervised pair mining: From the deepvk/cultura_ru_edu corpus, we extract 50M pairs using a simple heuristic—selecting non-overlapping text blocks that are not substrings of one another.

This approach has shown promising results, allowing us to train high-performing models with minimal target-language pairs—especially when compared to pipelines used for other languages.

The table below shows the datasets used and the number of times each was upsampled.

Dataset	Size	Upsample
nomic-en	235M	1
nomic-ru	39M	3
in-house En-Ru parallel	250M	1
cultura-sampled	50M	1
Total	652M

For the third stage, we switch to cleaner, task-specific datasets.
In some cases, additional filtering was applied using a cross-encoder.
For all retrieval datasets, we mine hard negatives.

Dataset	Examples	Notes
Nomic-en-supervised	1.7 M	Unmodified
AllNLI	200 K	Translated SNLI/MNLI/ANLI to Russian
fishkinet-posts	93 K	Title–content pairs
gazeta	55 K	Title–text pairs
habr_qna	100 K	Title–description pairs
lenta	100 K	Title–news pairs
miracl_ru	10 K	One positive per anchor
mldr_ru	1.8 K	Unmodified
mr-tydi_ru	5.3 K	Unmodified
mmarco_ru	500 K	Unmodified
ru-HNP	100 K	One pos + one neg per anchor
ru‑queries	199 K	In-house (generated as in arXiv:2401.00368)
ru‑WaNLI	35 K	Entailment -> pos, contradiction -> neg
sampled_wiki	1 M	Sampled text blocks from Wikipedia
summ_dialog_news	37 K	Summary–info pairs
wikiomnia_qna	100 K	QA pairs (T5-generated)
yandex_q	83 K	Q+desc-answer pairs
Total	4.3 M

Ablation

Alongside the final model, we also release all intermediate training steps.
Both the retromae and weakly_sft models are available under the specified revisions in this repository.
We hope these additional models prove useful for your experiments.

Below is a comparison of all training stages on a subset of MTEB-rus.

Citations

@misc{deepvk2025user,
    title={USER2},
    author={Malashenko, Boris and Spirin, Egor and Sokolov Andrey},
    url={https://huggingface.co/deepvk/USER2-small},
    publisher={Hugging Face}
    year={2025},
}