Model reference · open weights
rubert-cased-sentence is an open-weight embedding model from DeepPavlov. 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 | DeepPavlov |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Context | 512 tokens |
| Runs with | transformers |
| Released | 2022-03-02 |
| Popularity | 13k downloads / month |
| Licence | Unknown |
About
Sentence RuBERT (Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI[1] google-translated to russian and on russian part of XNLI dev set[2]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT[3].
[1]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. (2015) A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326
[2]: Williams A., Bowman S. (2018) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint arXiv:1809.05053
[3]: N. Reimers, I. Gurevych (2019) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint arXiv:1908.10084
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys rubert-cased-sentence for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (rubert-cased-sentence 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-cased-sentence","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.