Multilingual BERT Engine

Model Specifications

Aevum-MLBERT-140 extends the original Multilingual BERT architecture with continuous pretraining on verified encyclopedia corpora, academic literature, and parallel translation datasets. Optimized for low-latency inference and cross-lingual transfer learning.

Parameters

248M

Dense transformer layers optimized for CPU/GPU inference

Layers / Heads

12 / 12

Standard BERT-base configuration with multi-head attention

Hidden Size

768

Embedding dimension for cross-lingual semantic space

Max Sequence

512 Tokens

Supports long-form academic and encyclopedia passages

What It Powers

🌐

Cross-Lingual Semantic Search

Query in any supported language and retrieve conceptually relevant articles across all languages without explicit translation steps.

🔗

Entity Alignment & Disambiguation

Maps synonymous and polysemous entities across language clusters using contextual embeddings and knowledge graph priors.

⚡

Zero-Shot Transfer

Deploy trained classifiers for sentiment, topic, or toxicity detection in low-resource languages without additional fine-tuning.

📊

Dynamic Knowledge Graph Injection

Real-time fusion of graph embeddings with transformer attention for context-aware relational reasoning.

Cross-Lingual Benchmarks

Evaluated on XCOPA, XNLI, and Aevum's internal CrossWikiQA dataset. Results represent micro-averaged F1 scores across 140 languages.

Benchmark	Language Group	Baseline (mBERT)	Aevum-MLBERT-140	Improvement
XCOPA (Reasoning)	High-Resource (en, de, fr, es)	68.2%	74.8%	+6.6
XCOPA (Reasoning)	Low-Resource (sw, yo, ml, te)	41.5%	58.3%	+16.8
XNLI (Classification)	Cross-Lingual Transfer	62.4%	71.1%	+8.7
CrossWikiQA	Entity Alignment F1	79.3%	91.6%	+12.3

API Reference & Usage

Access the Multilingual BERT engine via REST API or our Python/TypeScript SDK. All endpoints support streaming, batching, and async execution.

POST /v1/mlbert/embed

Generates dense 768-dimensional embeddings for text input. Supports multi-language batch processing and tokenization mode selection.

POST /v1/mlbert/search

Semantic similarity search across the encyclopedia corpus. Returns ranked articles with cross-lingual relevance scores.

Python Example

import requests

payload = {
    "text": ["Quantum entanglement", "ميكانيكا الكم", "クオンツ計算"],
    "model": "aevum-mlbert-140-v3.2",
    "return_type": "dense"
}

headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

response = requests.post("https://api.aevumencyclopedia.com/v1/mlbert/embed", json=payload, headers=headers)
embeddings = response.json()["data"]

# Each embedding is a 768-dim vector aligned in cross-lingual space
for i, vec in enumerate(embeddings):
    print(f"Lang {i}: shape {vec.shape}")

How Aevum Uses Ml-BERT

The engine is deeply integrated into our knowledge pipeline, enabling real-time cross-lingual understanding without compromising editorial integrity.

📝 Article Translation Assist

Auto-generates semantic drafts for new language editions, flagging low-confidence regions for expert review.

🔍 Semantic Search Index

Replaces keyword matching with dense vector retrieval, understanding intent across dialects and technical jargon.

🛡️ Content Moderation

Detects harmful, biased, or unverified claims across 140 languages using zero-shot toxicity classification.