embedding
SEA-LION-E5-Embedding-600M
AI Singapore SEA-LION-E5-Embedding-600M encoder or embedding model.
Levelmodel
Classembedding
Parameters600M
Contextnot in the notes
ReleasedOct 2026
Opennessunknown
Runs onCPU server
Statusactive
Lineage
finetune
E5 is the parent (finetune)low confidencenotesWhere this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 2 models. Confidence and evidence are the notes' own.
Industry Use Cases
Public Sector
Embeddings and retrieval for SEA languagesR&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 E5-style embedding
- Total parameters
- 600M 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
- MIT License sources
- Access routes
- open weights download
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
- listed by developer
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- 11 languages: Burmese, Chinese, English, Filipino, Indonesian, Khmer, Lao, Malay, Tamil, Thai, Vietnamese
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.
Links
DeveloperAI Singaporenational lab, Singapore
AI Singaporenational lab, SingaporeModelSEA-LION-E5-Embedding-600MAI Singapore, embedding
SEA-LION-E5-Embedding-600MAI Singapore, embeddingR&D TopicsLow-Resource and Regional Language ResourcesRetrieval-Augmented Generation (RAG)Text Embeddings for Semantic Search
Who Runs Itnone recordedDeployments of these use cases; none names this model.
Organisations 1
R&D topics 3
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…exemplarLow-Resource and Regional Language ResourcesDatasets, benchmarks and adaptation for under-served languages.produced by
Sources
- aisingapore (AI Singapore) huggingface.co https://huggingface.co/aisingapore
- huggingface.co/aisingapore/SEA-LION-E5-Embedding-600M https://huggingface.co/aisingapore/SEA-LION-E5-Embedding-600M
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "SEA-LION-E5-Embedding-600M", https://aurion.semai.id/models/ai-singapore-sea-lion-e5-embedding-600m/, records of October 2026.