Kazakh-E5-RAG-Embedding: Kazakh Embedding Model for RAG

Kazakh-E5-RAG-Embedding is an E5-style text embedding model for Kazakh RAG, semantic search, FAQ search, question-answer retrieval, and document search.

🏆 Best evaluated BASE-size embedding model on our Kazakh hard-negative retrieval benchmark.

The model is built on the multilingual-e5-base architecture and further optimized for Kazakh question-passage matching, hard-negative retrieval, and Kazakh Wikipedia-style document search.

Why use this model?

• Kazakh-focused retrieval: optimized for Kazakh questions, passages, and document search
• 🔍 Hard-negative ranking: designed to distinguish correct passages from very similar incorrect passages
• ⚡ Efficient BASE-size model: 278M parameters
• 🧠 E5-style format: uses query: and passage: prefixes
• 🔧 RAG-ready: works with sentence-transformers, vector databases, and retrieval pipelines
• 📊 Evaluated on 3 Kazakh retrieval benchmarks

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Usage

Installation

pip install sentence-transformers numpy

Convert Text to Embeddings

This model converts Kazakh text into 768-dimensional embedding vectors that can be used for semantic search, retrieval, and RAG.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("shyngys879/kazakh-e5-rag-embedding")

text = "passage: Астана — Қазақстан Республикасының астанасы."

embedding = model.encode(text, normalize_embeddings=True)

print(embedding.shape)
# (768,)

print(embedding[:5])
# Example output: [0.021, -0.034, 0.056, ...]

Basic Usage

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("shyngys879/kazakh-e5-rag-embedding")

query = "query: Қазақстанның астанасы қай қала?"
passage = "passage: Астана — Қазақстан Республикасының астанасы."

query_embedding = model.encode(query, normalize_embeddings=True)
passage_embedding = model.encode(passage, normalize_embeddings=True)

# Since embeddings are normalized, dot product = cosine similarity
similarity = np.dot(query_embedding, passage_embedding)

print(f"Similarity: {similarity:.4f}")

FAQ / Document Search

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("shyngys879/kazakh-e5-rag-embedding")

documents = [
    "Астана — Қазақстан Республикасының астанасы.",
    "Алматы — Қазақстанның ең үлкен қаласы.",
    "Қазақстан — Орталық Азиядағы мемлекет.",
    "Абай Құнанбайұлы — қазақтың ұлы ақыны және ағартушысы.",
]

query = "Қазақстанның астанасы қай қала?"

doc_embeddings = model.encode(
    ["passage: " + doc for doc in documents],
    normalize_embeddings=True
)

query_embedding = model.encode(
    "query: " + query,
    normalize_embeddings=True
)

scores = np.dot(doc_embeddings, query_embedding)
best_idx = np.argmax(scores)

print("Question:", query)
print("Best match:", documents[best_idx])
print("Score:", float(scores[best_idx]))

This same pattern can be used for FAQ search, document retrieval, and RAG pipelines: encode your documents, retrieve the most relevant passages, then pass them to an LLM as context.

Important Prefix Format

For best results, use E5-style prefixes:

Input type	Prefix
Query / question	query: ...
Passage / document	passage: ...

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Benchmark Results

The model was evaluated on three Kazakh retrieval settings:

1. OfficialKazQAD-HardTFIDF99 — hard-negative question-passage retrieval.
2. WikiFullCorpus — Kazakh Wikipedia-style full-corpus retrieval.
3. KazQAD-100 local — KazQAD-style retrieval with 100 candidates per query.

MRR means Mean Reciprocal Rank: higher is better, and it rewards models that rank the correct passage closer to the top.

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OfficialKazQAD-HardTFIDF99

This benchmark uses 1,929 KazQAD test queries.
For each query, the model must rank the correct passage among 100 candidate passages.

The candidate set contains:

• 1 correct passage
• 99 TF-IDF hard negatives

A hard negative is an incorrect passage that is textually or topically similar to the query. This makes the task harder than retrieval with random negative passages, because the model must identify which similar passage actually answers the question.

Model	Hits@1	Hits@5	MRR	Params
Kazakh-E5-RAG-Embedding	35.98%	72.47%	0.5189	278M
KazEmbed-V5 original	30.07%	65.68%	0.4619	278M
multilingual-e5-large	30.17%	62.99%	0.4490	560M
multilingual-e5-base	25.87%	56.71%	0.4048	278M
paraphrase-multilingual-mpnet-base-v2	10.01%	29.91%	0.2082	278M
LaBSE	7.47%	26.75%	0.1821	471M

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Additional Benchmarks

Benchmark	Model	Hits@1	Hits@5	MRR	Params
WikiFullCorpus	multilingual-e5-large	69.13%	85.79%	0.7656	560M
WikiFullCorpus	multilingual-e5-base	65.85%	80.87%	0.7290	278M
WikiFullCorpus	Kazakh-E5-RAG-Embedding	60.38%	74.32%	0.6689	278M
WikiFullCorpus	KazEmbed-V5 original	56.83%	70.49%	0.6276	278M
KazQAD-100 local	Kazakh-E5-RAG-Embedding	91.55%	100.00%	0.9554	278M
KazQAD-100 local	KazEmbed-V5 original	87.32%	100.00%	0.9334	278M
KazQAD-100 local	multilingual-e5-large	86.27%	98.24%	0.9189	560M
KazQAD-100 local	multilingual-e5-base	85.56%	97.54%	0.9105	278M

Benchmark notes:

• WikiFullCorpus evaluates full-corpus retrieval over Kazakh Wikipedia-style passages. The model must find the correct passage from a larger document corpus, which makes this closer to practical semantic search and RAG.
• KazQAD-100 local evaluates Kazakh question-passage retrieval with 100 candidates per query. It is a supporting benchmark for checking whether the model ranks the correct answer passage near the top.

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Model Details

Field	Value
Model name	shyngys879/kazakh-e5-rag-embedding
Model type	Text embedding / bi-encoder retrieval model
Architecture family	multilingual-e5-base / XLM-RoBERTa
Continued fine-tuning from	Nurlykhan/kazembed-v5
Model lineage	intfloat/multilingual-e5-base → Nurlykhan/kazembed-v5 → this model
Parameters	278M
Embedding dimension	768
Main language	Kazakh
Task	Retrieval, semantic search, RAG, question-passage matching
Training objective	Kazakh retrieval / question-passage matching
Training data	KazQAD-style retrieval data, TF-IDF hard negatives, Kazakh Wikipedia-style retrieval examples

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Recommended Use Cases

• Kazakh RAG systems
• Kazakh semantic search
• FAQ search
• document retrieval
• question-answer retrieval
• educational search
• Kazakh Wikipedia / encyclopedic search
• multilingual projects involving Kazakh

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Limitations

• The model is optimized mainly for Kazakh retrieval and RAG.
• On WikiFullCorpus, multilingual-e5-base and multilingual-e5-large perform better.

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Citation

@misc{kazakh-e5-rag-embedding-2026,
  title={Kazakh-E5-RAG-Embedding: A Kazakh Retrieval Embedding Model for RAG},
  author={Shyngys Sovetkhan},
  year={2026},
  howpublished={Hugging Face Model Hub},
  url={https://huggingface.co/shyngys879/kazakh-e5-rag-embedding}
}

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Acknowledgements

This model builds on:

• Nurlykhan/kazembed-v5
• intfloat/multilingual-e5-base
• KazQAD / KazQAD-style retrieval data
• Kazakh Wikipedia-style retrieval examples