USER

Universal Sentence Encoder for Russian (USER) is a sentence-transformer model for extracting embeddings exclusively for Russian language. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

This model is initialized from deepvk/deberta-v1-base and trained to work exclusively with the Russian language. Its quality on other languages was not evaluated.

Usage

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer

queries = [
  "Когда был спущен на воду первый миноносец «Спокойный»?",
  "Есть ли нефть в Удмуртии?"
]
passages = [
  "Спокойный (эсминец)\nЗачислен в списки ВМФ СССР 19 августа 1952 года.",
  "Нефтепоисковые работы в Удмуртии были начаты сразу после Второй мировой войны в 1945 году и продолжаются по сей день. Добыча нефти началась в 1967 году."
]

model = SentenceTransformer("deepvk/USER-base")
# Prompt should be specified according to the task (either 'query' or 'passage').
passage_embeddings = model.encode(passages, normalize_embeddings=True, prompt_name='passage')
# For tasks other than retrieval, you can simply use the `query` prompt, which is set by default.
query_embeddings = model.encode(queries, normalize_embeddings=True)

However, you can use model directly with transformers

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

def average_pool(
  last_hidden_states: Tensor,
  attention_mask: Tensor
) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(
      ~attention_mask[..., None].bool(), 0.0
    )
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

# You should manually add prompts when using the model directly. Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = [
  "query: Когда был спущен на воду первый миноносец «Спокойный»?",
  "query: Есть ли нефть в Удмуртии?",
  "passage: Спокойный (эсминец)\nЗачислен в списки ВМФ СССР 19 августа 1952 года.",
  "passage: Нефтепоисковые работы в Удмуртии были начаты сразу после Второй мировой войны в 1945 году и продолжаются по сей день. Добыча нефти началась в 1967 году."
]

tokenizer = AutoTokenizer.from_pretrained("deepvk/USER-base")
model = AutoModel.from_pretrained("deepvk/USER-base")

batch_dict = tokenizer(
  input_texts, padding=True, truncation=True, return_tensors="pt"
)
with inference_mode():
  outputs = model(**batch_dict)
  embeddings = average_pool(
    outputs.last_hidden_state, batch_dict["attention_mask"]
  )
  embeddings = F.normalize(embeddings, p=2, dim=1)

# Scores for query-passage
scores = (embeddings[:2] @ embeddings[2:].T) * 100
# [[55.86, 30.95],
#  [22.82, 59.46]]
print(scores.round(decimals=2))

⚠️ Attention ⚠️

Each input text should start with "query: " or "passage: ". For tasks other than retrieval, you can simply use the "query: " prefix.

Training Details

We aimed to follow the bge-base-en model training algorithm, but we made several improvements along the way.

Initialization: deepvk/deberta-v1-base

First-stage: Contrastive pre-training with weak supervision on the Russian part of mMarco corpus.

Second-stage: Supervised fine-tuning two different models based on data symmetry and then merging via LM-Cocktail:

1. We modified the instruction design by simplifying the multilingual approach to facilitate easier inference. For symmetric data (S1, S2), we used the instructions: "query: S1" and "query: S2", and for asymmetric data, we used "query: S1" with "passage: S2".

2. Since we split the data, we could additionally apply the AnglE loss to the symmetric model, which enhances performance on symmetric tasks.

3. Finally, we combined the two models, tuning the weights for the merger using LM-Cocktail to produce the final model, USER.

Dataset

During model development, we additional collect 2 datasets: deepvk/ru-HNP and deepvk/ru-WANLI.