ru-en-RoSBERTa EU AI Act Compliance Profile

Model Card for ru-en-RoSBERTa

The ru-en-RoSBERTa is a general text embedding model for Russian. The model is based on ruRoBERTa and fine-tuned with ~4M pairs of supervised, synthetic and unsupervised data in Russian and English. Tokenizer supports some English tokens from RoBERTa tokenizer.

For more model details please refer to our article.

Usage

The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.

We use the following basic rules to choose a prefix:

• "search_query: " and "search_document: " prefixes are for answer or relevant paragraph retrieval
• "classification: " prefix is for symmetric paraphrasing related tasks (STS, NLI, Bitext Mining)
• "clustering: " prefix is for any tasks that rely on thematic features (topic classification, title-body retrieval)

To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets.

Below are examples of texts encoding using the Transformers and SentenceTransformers libraries.

Transformers

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


def pool(hidden_state, mask, pooling_method="cls"):
    if pooling_method == "mean":
        s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
        d = mask.sum(axis=1, keepdim=True).float()
        return s / d
    elif pooling_method == "cls":
        return hidden_state[:, 0]

inputs = [
    # 
    "classification: Он нам и <unk> не нужон ваш Интернет!",
    "clustering: В Ярославской области разрешили работу бань, но без посетителей",
    "search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",

    # 
    "classification: What a time to be alive!",
    "clustering: Ярославским баням разрешили работать

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Library

Base Model