embedding
SEA-LION ModernBERT-Embedding 300M
AI Singapore SEA-LION ModernBERT-Embedding 300M encoder or embedding model.
Levelmodel
Classembedding
Parameters300M
Contextnot in the notes
ReleasedOct 2026
Opennessunknown
Runs onCPU server
Statusactive
R&D Topics
Identity
- Developer
- AI Singapore
- Family
- SEA-LION
- Released
- Oct 2026 sourcesregistry date
- Weights published
- –
- Record checked
- 10 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- other ModernBERT encoder
- Total parameters
- 300M sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- text
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- unknown
- Licence
- not in the notes sources
- Access routes
- not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- CPU server
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- SEA languages incl. Indonesian (unverified list)
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperAI Singaporenational lab, Singapore
AI Singaporenational lab, SingaporeModelSEA-LION ModernBERT-Embedding 300MAI Singapore, embedding
SEA-LION ModernBERT-Embedding 300MAI Singapore, embeddingR&D TopicsLow-Resource and Regional Language ResourcesPretrained Bidirectional Text EncodersRetrieval-Augmented Generation (RAG)Text Embeddings for Semantic Search
Use CasesEmbeddings and Retrieval for SEA LanguagesPublic SectorIndonesian NLU (Classification, NER)Public SectorIndonesian and Regional-Language LLMsPublic SectorTranslation Including IndonesianPublic Sector+5 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Bank Tabungan Negara (BTN)In production, 1 Jul 2026
PT PegadaianIn production, 5 Jun 2026
Bank Central Asia (BCA)In production, 16 Mar 2026Bank Central Asia (BCA)In production, 16 Mar 2026+13 moreDeployments of these use cases; none names this model.
PT Bank Tabungan Negara (BTN)In production, 1 Jul 2026PT PegadaianIn production, 5 Jun 2026Bank Central Asia (BCA)In production, 16 Mar 2026Bank Central Asia (BCA)In production, 16 Mar 2026+13 moreDeployments of these use cases; none names this model.Models 1
Organisations 1
R&D topics 4
Retrieval-Augmented Generation (RAG)Grounding generation in retrieved documents.used byText Embeddings for Semantic SearchTraining encoders that turn text in many languages into vectors so that search, clusterin…produced byLow-Resource and Regional Language ResourcesDatasets, benchmarks and adaptation for under-served languages.produced byPretrained Bidirectional Text EncodersPretraining BERT-style encoder-only models that read text in both directions, used for cl…produced by
Sources
- aisingapore (AI Singapore) huggingface.co https://huggingface.co/aisingapore
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "SEA-LION ModernBERT-Embedding 300M", https://aurion.semai.id/models/ai-singapore-sea-lion-modernbert-embedding-300m/, records of October 2026.