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USER-bge-m3

Name: USER-bge-m3
Availability: InStock

Universal Sentence Encoder for Russian (USER) is a sentence-transformer model for extracting embeddings exclusively for Russian language. It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.

This model is initialized from TatonkaHF/bge-m3_en_ru which is shrinked version of baai/bge-m3 model and trained to work mainly 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:

python

from sentence_transformers import SentenceTransformer


input_texts = [
  "Когда был спущен на воду первый миноносец «Спокойный»?",
  "Есть ли нефть в Удмуртии?",
  "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-bge-m3")
embeddings = model.encode(input_texts, normalize_embeddings=True)

However, you can use model directly with transformers

python

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


input_texts = [
  "Когда был спущен на воду первый миноносец «Спокойный»?",
  "Есть ли нефть в Удмуртии?",
  "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."
]


tokenizer = AutoTokenizer.from_pretrained("deepvk/USER-bge-m3")
model = AutoModel.from_pretrained("deepvk/USER-bge-m3")
model.eval()


encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
  model_output = model(**encoded_input) 
  # Perform pooling. In this case, cls pooling.
  sentence_embeddings = model_output[0][:, 0]

# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)

# [[0.5567, 0.3014],
#  [0.1701, 0.7122]]
scores = (sentence_embeddings[:2] @ sentence_embeddings[2:].T)

Also, you can use native FlagEmbedding library for evaluation. Usage is described in bge-m3 model card.

Training Details

We follow the USER-base model training algorithm, with several changes as we use different backbone.

Initialization: TatonkaHF/bge-m3_en_ru – shrinked version of baai/bge-m3 to support only Russian and English tokens.

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

Since we split the data, we could additionally apply the AnglE loss to the symmetric model, which enhances performance on symmetric tasks.
Finally, we added the original bge-m3 model to the two obtained models to prevent catastrophic forgetting, tuning the weights for the merger using LM-Cocktail to produce the final model, USER-bge-m3.

Dataset

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

Symmetric Dataset	Size	Asymmetric Dataset	Size
AllNLI	282 644	MIRACL	10 000
MedNLI	3 699	MLDR	1 864
RCB	392	Lenta	185 972
Terra	1 359	Mlsum	51 112
Tapaco	91 240	Mr-TyDi	536 600
deepvk/ru-WANLI	35 455	Panorama	11 024
deepvk/ru-HNP	500 000	PravoIsrael	26 364
		Xlsum	124 486
		Fialka-v1	130 000
		RussianKeywords	16 461
		Gazeta	121 928
		Gsm8k-ru	7 470
		DSumRu	27 191
		SummDialogNews	75 700

Total positive pairs: 2,240,961 Total negative pairs: 792,644 (negative pairs from AIINLI, MIRACL, deepvk/ru-WANLI, deepvk/ru-HNP)

For all labeled datasets, we only use its training set for fine-tuning. For datasets Gazeta, Mlsum, Xlsum: pairs (title/text) and (title/summary) are combined and used as asymmetric data.

AllNLI is an translated to Russian combination of SNLI, MNLI and ANLI.

Experiments

We compare our mode with the basic baai/bge-m3 on the encodechka benchmark. In addition, we evaluate model on the russian subset of MTEB on Classification, Reranking, Multilabel Classification, STS, Retrieval, and PairClassification tasks. We use validation scripts from the official repositories for each of the tasks.

Results on encodechka:

Model	Mean S	Mean S+W	STS	PI	NLI	SA	TI	IA	IC	ICX	NE1	NE2
`baai/bge-m3`	0.787	0.696	0.86	0.75	0.51	0.82	0.97	0.79	0.81	0.78	0.24	0.42
`USER-bge-m3`	0.799	0.709	0.87	0.76	0.58	0.82	0.97	0.79	0.81	0.78	0.28	0.43

Results on MTEB:

Type	`baai/bge-m3`	`USER-bge-m3`
Average (30 datasets)	0.689	0.706
Classification Average (12 datasets)	0.571	0.594
Reranking Average (2 datasets)	0.698	0.688
MultilabelClassification (2 datasets)	0.343	0.359
STS Average (4 datasets)	0.735	0.753
Retrieval Average (6 datasets)	0.945	0.934
PairClassification Average (4 datasets)	0.784	0.833

Limitations

We did not thoroughly evaluate the model's ability for sparse and multi-vec encoding.

Citations

@misc{deepvk2024user,
    title={USER: Universal Sentence Encoder for Russian},
    author={Malashenko, Boris and  Zemerov, Anton and Spirin, Egor},
    url={https://huggingface.co/datasets/deepvk/USER-base},
    publisher={Hugging Face}
    year={2024},
}