Model reference · open weights
borealis-embed is an open-weight embedding model from NbAiLab. 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 | NbAiLab |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 212M |
| Context | 8k tokens |
| Runs with | sentence-transformers |
| Released | 2026-09-01 |
| Popularity | 550 downloads / month |
| Licence | Commercial licence needed |
About
NbAiLab/borealis-embed-212m is a 212M parameter sentence-transformers embedding model (98M embedding parameters) based on an adapted Gemma3Text architecture. The model is pretrained from scratch on the open subset of our Aurora 2604 data and fine-tuned as a sentence transformer with mean pooling on subsets from NbAiLab/mnli-norwegian, DDSC/nordic-embedding-training-data, sentence-transformers/parallel-sentences-opus-100, lightonai/embeddings-pre-training, and internal newspaper articles. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval. With a longer max sequence length than its predecessors (nb-sbert and nb-sbert-v2), the model can handle chunks of text up to 8192 tokens long.
This release is a non-generative encoder model whose outputs are vectors/scores rather than language or media. Its intended functionality is limited to representation, retrieval, ranking, or classification support. On that basis, the release is preliminarily assessed as not falling within the provider obligations for GPAI models under the EU AI Act definitions, subject to legal confirmation if capability scope or marketed generality changes. For more information, see the Model Documentation Form.
SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'AustralisModel'})
(1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("NbAiLab/borealis-embed-212M")
# Run inference
queries = [
'- Who is he?',
]
documents = [
'- Hvem er han?',
'Banning.',
'Nei, det har hun ikke.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7765, 0.2560, 0.2525]])
We evaluated this model on two benchmarks: the Scandinavian Embedding Benchmark (SEB) and the Long-context Retrieval Benchmark (LongEmbed) from MTEB.
During pretraining, max sequence length of the model was extended to 8192 tokens. However, during the fine-tuning process, the model is trained with a sequence length 2048. The evaluation results on LongEmbed show that the model has retained the length extension from pretraining and confirms that the model can support up to 8192 tokens.
"Scandinavian text embedding quality covering Danish, Swedish, Norwegian Bokmål, and Nynorsk and spanning classification, clustering, retrieval as well as bitext tasks across dialects or written forms."
| Task | nb-sbert-v2-base | nb-sbert-v2-large | borealis-embed-212M |
|---|---|---|---|
| Mean (Task) | 0.5496 | 0.5638 | 0.5891 |
| Mean (TaskType) | 0.5690 | 0.5770 | 0.6037 |
| & |
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys borealis-embed for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (borealis-embed 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":"borealis-embed","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.