Model reference · open weights
rubert-mini-frida is an open-weight embedding model from sergeyzh. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | sergeyzh |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 32M |
| Context | 2k tokens |
| Runs with | sentence-transformers |
| Based on | sergeyzh/rubert-mini-sts |
| Released | 2025-03-02 |
| Popularity | 6k downloads / month |
| Licence | Open weights |
About
Модель для расчетов эмбеддингов предложений на русском и английском языках получена методом дистилляции эмбеддингов ai-forever/FRIDA (размер эмбеддингов - 1536, слоёв - 24) в sergeyzh/rubert-mini-sts (размер эмбеддингов - 312, слоёв - 7). Основной режим использования FRIDA - CLS pooling заменен на mean pooling. Каких-либо других изменений поведения модели (модификации или фильтрации эмбеддингов, использования дополнительной модели) не производилось. Дистиляция выполнена в максимально возможном объеме - эмбеддинги русских и английских предложений, работа префиксов.
Рекомендуемый размер контекста модели соответствует FRIDA и не превышает 512 токенов (фактический унаследованный от исходной модели - 2048).
Все префиксы унаследованы от FRIDA. Оптимальный (обеспечивающий средние результаты) для большинства задач - "categorize: " прописан по умолчанию в config_sentence_transformers.json
Перечень используемых префиксов и их влияние на оценки модели в encodechka:
| Префикс | STS | PI | NLI | SA | TI |
|---|---|---|---|---|---|
| - | 0.839 | 0.762 | 0.475 | 0.801 | 0.972 |
| search_query: | 0.846 | 0.761 | 0.498 | 0.800 | 0.973 |
| search_document: | 0.830 | 0.748 | 0.468 | 0.794 | 0.972 |
| paraphrase: | 0.835 | 0.764 | 0.475 | 0.799 | 0.973 |
| categorize: | 0.850 | 0.761 | 0.516 | 0.802 | 0.973 |
| categorize_sentiment: | 0.755 | 0.656 | 0.427 | 0.798 | 0.959 |
| categorize_topic: | 0.734 | 0.523 | 0.389 | 0.728 | 0.959 |
| categorize_entailment: | 0.837 | 0.753 | 0.544 | 0.802 | 0.970 |
Задачи:
Оценки модели на бенчмарке ruMTEB:
| Model Name | Metric | Frida | rubert-mini-frida | multilingual-e5-large-instruct | multilingual-e5-large |
|---|---|---|---|---|---|
| CEDRClassification | Accuracy | 0.646 | 0.552 | 0.500 | 0.448 |
| GeoreviewClassification | Accuracy | 0.577 | 0.464 | 0.559 | 0.497 |
| GeoreviewClusteringP2P | V-measure | 0.783 | 0.698 | 0.743 | 0.605 |
| HeadlineClassification | Accuracy | 0.890 | 0.880 | 0.862 | 0.758 |
| InappropriatenessClassification | Accuracy | 0.783 | 0.698 | 0.655 | 0.616 |
| KinopoiskClassification | Accuracy | 0.705 | 0.595 | 0.661 | 0.566 |
| RiaNewsRetrieval | NDCG@10 | 0.868 | 0.721 | 0.824 | 0.807 |
| RuBQReranking | MAP@10 | 0.771 | 0.711 | 0.717 | 0.756 |
| RuBQRetrieval | NDCG@10 | 0.724 | 0.654 | 0.692 | 0.741 |
| RuReviewsClassification | Accuracy | 0.751 | 0.658 | 0.686 | 0.653 |
| RuSTSBenchmarkSTS | Pearson correlation | 0.814 | 0.803 | 0.840 | 0.831 |
| RuSciBenchGRNTIClassification | Accuracy | 0.699 | 0.625 | 0.651 | 0.582 |
| RuSciBenchGRNTIClusteringP2P | V-measure | 0.670 | 0.586 | 0.622 | 0.520 |
| RuSciBenchOECDClassification | Accuracy | 0.546 | 0.493 | 0.502 | 0.445 |
| RuSciBenchOECDClusteringP2P | V-measure | 0.566 | 0.507 | 0.528 | 0.450 |
| SensitiveTopicsClassification | Accuracy | 0.398 | 0.373 | 0.323 | 0.257 |
| TERRaClassification | Average Precision | 0.665 | 0.606 | 0.639 | 0.584 |
| Model Name | Metric | Frida | rubert-mini-frida | multilingual-e5-large-instruct | multilingual-e5-large |
|---|---|---|---|---|---|
| Classification | Accuracy | 0.707 | 0.631 | 0.654 | 0.588 |
| Clustering | V-measure | 0.673 | 0.597 | 0.631 |
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys rubert-mini-frida for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (rubert-mini-frida below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"rubert-mini-frida","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.