sakthai-embedding-multilingual

<p align="center"> <strong>384-dim cross-lingual sentence embeddings · 50+ languages · CPU-friendly</strong><br/> <em>The retrieval stage of the SakThai pipeline · <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02">SakThai Model Family</a></em> </p> <p align="center"> <a href="https://huggingface.co/Nanthasit"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Nanthasit-6644cc" alt="Profile"/></a> <a href="https://github.com/beer-sakthai"><img src="https://img.shields.io/badge/GitHub-beer--sakthai-181717?logo=github" alt="GitHub"/></a> <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/%F0%9F%8F%A0-SakThai%20Family-6644cc" alt="Collection"/></a> <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2FNanthasit%2Fsakthai-embedding-multilingual&query=%24.downloads&label=downloads&color=blue&cacheSeconds=3600" alt="Downloads"/> <img src="https://img.shields.io/badge/dim-384-blue" alt="384d"/> <img src="https://img.shields.io/badge/base-MiniLM%20L12--H384-6644cc" alt="Base: Multilingual-MiniLM-L12-H384"/> <img src="https://img.shields.io/badge/license-MIT-green" alt="MIT"/> </p>

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Model Description

SakThai Multilingual Embedding is a BERT-based sentence-transformers model producing 384-dimensional cross-lingual embeddings. Sentences with similar meaning map close together regardless of language — enabling multilingual retrieval and comparison without translation.

What makes it special:

• 🌍 50+ languages (multilingual MiniLM vocabulary, 250K tokens)
• 🖥️ CPU inference — verified locally (see Evaluation & Verification)
• 💾 Lightweight architecture — ~118M parameters, 470 MB fp32 safetensors
• 🔗 Cross-lingual — query in English, retrieve in Thai, French, Japanese, etc.
• 📦 Perfect for multilingual RAG pipelines
• 🚀 Fastest-growing SakThai embedding model — 627 downloads, live 2026-08-01

Quick Start

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Nanthasit/sakthai-embedding-multilingual")

sentences = [
    "I love machine learning",
    "J'adore l'apprentissage automatique",
    "Ich liebe maschinelles Lernen",
    "Me encanta el aprendizaje automático",
    "ฉันชอบการเรียนรู้ของเครื่อง",
    "我喜欢机器学习",
]
embeddings = model.encode(sentences)
print(embeddings.shape)  # (6, 384)

from sklearn.metrics.pairwise import cosine_similarity
sim = cosine_similarity([embeddings[0]], [embeddings[1]])
print(f"Cross-lingual similarity: {sim[0][0]:.3f}")

Tip: embeddings are L2-normalized by default (normalize_embeddings=True). For large corpora, use model.encode(docs, batch_size=32) to avoid memory spikes.

Batch Encoding for Large Corpora

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Nanthasit/sakthai-embedding-multilingual")

# Stream from disk in chunks
def stream_docs(path, chunk_size=1000):
    with open(path) as f:
        chunk = []
        for line in f:
            chunk.append(line.strip())
            if len(chunk) >= chunk_size:
                yield chunk
                chunk = []
        if chunk:
            yield chunk

for docs in stream_docs("corpus.txt"):
    embs = model.encode(docs, batch_size=64, show_progress_bar=True)
    # write to FAISS / Qdrant / disk

Cross-lingual Semantic Search

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("Nanthasit/sakthai-embedding-multilingual")
docs = [
    "Neural networks are inspired by the brain.",
    "Les réseaux de neurones sont inspirés par le cerveau.",
    "Künstliche Intelligenz verändert die Welt.",
    "人工知能は世界を変える",
    "Machine learning is a subset of AI.",
]
doc_emb = model.encode(docs, convert_to_tensor=True)

query = "how do neural networks work?"
query_emb = model.encode(query, convert_to_tensor=True)
scores = util.cos_sim(query_emb, doc_emb)[0]
print(f"Best match: {docs[scores.argmax()]}")

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Architecture

Training Details

Evaluation & Verification

Local inference is verified (2026-07-30, committed to .eval_results/inference-check-20260730_232754.yaml):

Health check (.eval_results/health-check-sakthai-embedding-multilingual-2026-07-30-4.yaml):

• 📈 Download velocity rank: 2/19
• 📊 Download rank: 6/19 · Card quality: 90/100 · Repo hygiene: 90/100

Verified smoke benchmark (2026-08-01):

These scores come from live local inference and are saved in the repo's .eval_results/ history. This is a smoke check, not a formal MTEB run.

Hosted inference — honest status: the HF serverless router returns 400 Model not supported by provider hf-inference and api-inference.hf.co returns 403. For production, run locally with sentence-transformers or on a dedicated TEI endpoint.

Formal benchmarks (STS-B, MTEB-style retrieval): pending. No verified scores are published yet. As the base architecture is the same 12-layer / 384-dim multilingual MiniLM family as paraphrase-multilingual-MiniLM-L12-v2, expected STS performance is in that family's ballpark (~0.75–0.85 Spearman) — estimated, not yet verified. Proper cross-lingual retrieval and STS results will be published via the SakThai Leaderboard Space when available.

Deployment Options

When to Use

• Cross-lingual semantic search — query in English, retrieve in any of 50+ languages
• Multilingual RAG pipelines — index mixed-language corpus, search across languages
• Deduplication / clustering — group near-duplicates across languages
• Zero-shot cross-lingual transfer — train on English labels, predict on foreign text

Pipeline Integration

Limitations

• No verified MTEB/STS scores yet. Published numbers are base-model estimates, not this fine-tune’s measured results.
• Hosted inference is unsupported on the default HF inference router; requires local sentence-transformers or a dedicated TEI endpoint.
• Speed/throughput claims are relative. Local encode latency is single-device CPU; GPU/batch performance will differ.
• Quality varies by language pair. High-resource languages are stronger; low-resource cross-lingual pairs may degrade.
• Context is 512 tokens. Longer documents need chunking or a longer-context reranker.
• No multilingual benchmark card yet. Leaderboard results will appear when formally evaluated.
• Community model, not production SRE. Built on free-tier credits; no commercial SLA.
• Zero-budget constraint. We cannot run large-scale public MTEB/beIR benchmarks on paid compute yet.

SakThai Model Family 🏠

All 20 public SakThai models + 2 companion repos (downloads live, 2026-08-01):

Companion repos:

📦 View the whole family collection

The House of Sak 🏠

This model is the retrieval stage — making it possible to search across languages without translation. Built from a shelter in Cork, Ireland, with $0 budget and a belief that AI should be for everyone.

"We are one family — and becoming more." — Beer (beer-sakthai)

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Support

• ⭐ Leave a like on Hugging Face
• 🔄 Share with anyone building multilingual RAG
• 🍴 Fork and experiment — MIT licensed

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License

MIT — free to use, modify, and share.

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Citation

@misc{sakthai-multilingual-embedding-2026,
  title  = {SakThai Multilingual Embedding},
  author = {Beer (beer-sakthai) and SakThai},
  year   = {2026},
  url    = {https://huggingface.co/Nanthasit/sakthai-embedding-multilingual}
}

If you use the base architecture, also cite:

@misc{multilingual-minilm-2022,
  title  = {Multilingual MiniLM},
  author = {Wang, Liang and others},
  year   = {2022},
  url    = {https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384}
}

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"We are one family — and becoming more." 🏠