Multilingual Embeddings

Multilingual Embeddings

Model Landscape

Cross-Lingual Retrieval (query ≠ document language)

Scenario: Spanish query retrieves English documents.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("intfloat/multilingual-e5-large")

docs_en = [
    "passage: The European Central Bank raised interest rates by 25 basis points.",
    "passage: Quantum computing uses qubits instead of classical bits.",
]
query_es = "query: ¿Cuánto subió las tasas de interés el BCE?"

doc_vecs = model.encode(docs_en, normalize_embeddings=True)
qry_vec  = model.encode([query_es], normalize_embeddings=True)[0]

import numpy as np
scores = doc_vecs @ qry_vec
print(scores.argmax())  # should be 0

BGE-M3 multilingual + multi-functionality

BGE-M3 is currently the strongest open multilingual embedding when you want dense + sparse + colbert from a single forward pass.

from FlagEmbedding import BGEM3FlagModel

model = BGEM3FlagModel("BAAI/bge-m3", use_fp16=True)

queries = ["什么是量子计算？", "What is quantum computing?", "¿Qué es la computación cuántica?"]
docs = ["Quantum computing exploits superposition and entanglement to process information."]

q_out = model.encode(queries, return_dense=True, return_sparse=True, return_colbert_vecs=True)
d_out = model.encode(docs,    return_dense=True, return_sparse=True, return_colbert_vecs=True)

# Dense score
dense_scores = q_out["dense_vecs"] @ d_out["dense_vecs"].T

# Sparse (lexical) score
sparse_scores = model.compute_lexical_matching_score(
    q_out["lexical_weights"], d_out["lexical_weights"]
)

# ColBERT score
colbert_scores = [
    model.colbert_score(q_out["colbert_vecs"][i], d_out["colbert_vecs"][0])
    for i in range(len(queries))
]

# Weighted fusion (tune per corpus)
final = 0.4 * dense_scores + 0.2 * sparse_scores + 0.4 * colbert_scores

Cohere embed-multilingual-v3

import cohere

co = cohere.ClientV2()

docs_mixed = [
    "The Louvre is located in Paris.",
    "La Tour Eiffel a été construite en 1889.",
    "東京スカイツリーは634メートルです。",
]
doc_resp = co.embed(
    texts=docs_mixed,
    model="embed-multilingual-v3.0",
    input_type="search_document",
    embedding_types=["float"],
)
qry = co.embed(
    texts=["how tall is the Tokyo Skytree"],
    model="embed-multilingual-v3.0",
    input_type="search_query",
    embedding_types=["float"],
).embeddings.float[0]

LaBSE (translation-mining, sentence alignment)

Use LaBSE when the task is parallel corpus mining or near-duplicate cross-lingual sentence matching — not for QA retrieval where E5 / BGE-M3 outperform it.

from sentence_transformers import SentenceTransformer

labse = SentenceTransformer("sentence-transformers/LaBSE")

en = labse.encode(["The cat sat on the mat."], normalize_embeddings=True)
fr = labse.encode(["Le chat était assis sur le tapis."], normalize_embeddings=True)
print((en @ fr.T).item())  # ~0.95 — high alignment

Language-Specific Considerations

Chinese / Japanese / Korean

• No whitespace tokenization → your chunker must NOT assume split() by space.
• Use character-based or native segmenters (jieba for Chinese, fugashi for Japanese, kiwipiepy for Korean).

# Japanese chunking with fugashi
from fugashi import Tagger
tagger = Tagger()
tokens = [w.surface for w in tagger("東京は日本の首都です。")]

Arabic / Hebrew

• Right-to-left. Normalize diacritics and alef variants.
• Use unicodedata.normalize("NFKC", text) before embedding.

import unicodedata, re

def normalize_arabic(text: str) -> str:
    text = unicodedata.normalize("NFKC", text)
    text = re.sub(r"[\u064B-\u065F\u0670]", "", text)  # strip diacritics
    text = text.replace("أ", "ا").replace("إ", "ا").replace("آ", "ا")
    text = text.replace("ى", "ي").replace("ة", "ه")
    return text

Low-resource languages

OpenAI and Cohere degrade noticeably on Swahili, Yoruba, Bengali, Tamil, etc. BGE-M3 and multilingual-e5 generally win. Always run a small eval set in the target language before committing.

Code-Mixed Text (Hinglish, Spanglish, Singlish)

Code-mixed queries break most tokenizers. Strategies:

1. Keep text as-is; prefer BGE-M3 or multilingual-e5 (trained on web data with code-mixing).
2. Use a language-detection + per-language indexing pipeline if recall is critical.

from lingua import LanguageDetectorBuilder, Language

detector = (
    LanguageDetectorBuilder
    .from_languages(Language.ENGLISH, Language.HINDI, Language.SPANISH)
    .build()
)
text = "Mujhe ek coffee chahiye"
lang = detector.detect_language_of(text)
# Route to language-specific index if desired

Tokenizer Pitfalls

Evaluation

Never trust MTEB averages for your specific language. Build a small labelled set of (query, relevant-doc) pairs and measure MRR@10 / nDCG@10 per language.

from sklearn.metrics import ndcg_score
import numpy as np

def evaluate_per_language(model, eval_pairs_by_lang):
    for lang, pairs in eval_pairs_by_lang.items():
        queries = [p["query"] for p in pairs]
        all_docs = list({d for p in pairs for d in p["candidates"]})
        q_vecs = model.encode([f"query: {q}" for q in queries], normalize_embeddings=True)
        d_vecs = model.encode([f"passage: {d}" for d in all_docs], normalize_embeddings=True)
        scores = q_vecs @ d_vecs.T
        true_rel = np.zeros_like(scores)
        for i, p in enumerate(pairs):
            for d in p["relevant"]:
                true_rel[i, all_docs.index(d)] = 1
        print(f"{lang}: nDCG@10 = {ndcg_score(true_rel, scores, k=10):.3f}")

Anti-Patterns

Production Checklist

• Per-language eval set (at least 50 queries each)
• Tokenizer token-count checked on representative non-English docs
• Script-specific normalization (Arabic diacritics, CJK width, etc.)
• Language detection for routing if mixed-corpus
• Chunk size adjusted for CJK (shorter) vs Latin
• Fallback translation pipeline if low-resource quality too low
• Monitor per-language recall in production logs