bge-m3-legal-ru-cocktail-40-60

Model Details

Model Description

• Model Type: Sentence Transformer (LM-Cocktail)
• Base Models: BGE-M3 fine-tuned on Russian legal documents
• Maximum Sequence Length: 512 tokens (optimized for speed)
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Language: Russian
• Domain: Legal documents (court decisions, federal laws, regional legislation)
• License: MIT

Key Features

• ✅ Best Recall@5 among all tested models (91.79%)
• ✅ No prefix required (inherited from bge-m3-legal-ru-updata)
• ✅ Balanced performance across all legal document types
• ✅ Production-ready for Russian legal semantic search

Performance

Benchmark Results (dataset2: 7,187 test examples)

Performance by Dataset Type

Comparison with Parent Models

The cocktail demonstrates synergistic effect - it outperforms both parent models!

Usage

Installation

pip install -U sentence-transformers

Basic Usage

from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer("Roflmax/bge-m3-legal-ru-cocktail-40-60")
model.max_seq_length = 512  # Optimized for speed

# Example: Semantic search in legal documents
query = "Какое наказание предусмотрено за управление транспортным средством в состоянии опьянения?"

documents = [
    "Статья 264.1 УК РФ. Нарушение правил дорожного движения лицом, подвергнутым административному наказанию...",
    "КоАП РФ Статья 12.8. Управление транспортным средством водителем, находящимся в состоянии опьянения...",
    "Статья 228 УК РФ. Незаконные приобретение, хранение, перевозка, изготовление..."
]

# Encode
query_embedding = model.encode(query, normalize_embeddings=True)
doc_embeddings = model.encode(documents, normalize_embeddings=True)

# Calculate similarity
from sklearn.metrics.pairwise import cosine_similarity
similarities = cosine_similarity([query_embedding], doc_embeddings)[0]

# Get top results
top_indices = similarities.argsort()[::-1]
for idx in top_indices:
    print(f"Score: {similarities[idx]:.4f} | {documents[idx][:100]}...")

Batch Processing

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("Roflmax/bge-m3-legal-ru-cocktail-40-60")
model.max_seq_length = 512

# Batch encode documents
documents = [
    "Первый документ...",
    "Второй документ...",
    # ... more documents
]

# Process in batches for efficiency
embeddings = model.encode(
    documents,
    batch_size=32,
    normalize_embeddings=True,
    show_progress_bar=True
)

print(f"Generated {len(embeddings)} embeddings of dimension {embeddings.shape[1]}")

Semantic Search Pipeline

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("Roflmax/bge-m3-legal-ru-cocktail-40-60")

# Your corpus
corpus = [
    "Документ 1: содержание...",
    "Документ 2: содержание...",
    # ... more documents
]

# Encode corpus once
corpus_embeddings = model.encode(corpus, convert_to_tensor=True, normalize_embeddings=True)

# Query
query = "Ваш поисковый запрос"
query_embedding = model.encode(query, convert_to_tensor=True, normalize_embeddings=True)

# Search
hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=5)[0]

# Display results
for hit in hits:
    print(f"Score: {hit['score']:.4f} | {corpus[hit['corpus_id']][:100]}...")

Important Notes

No Prefix Required

Unlike some BGE models, this model does NOT require query/passage prefixes. Simply encode your text directly:

# ✅ Correct - no prefix needed
embedding = model.encode("Ваш текст")

# ❌ Not needed
embedding = model.encode("Represent this sentence for searching relevant passages: Ваш текст")

Sequence Length

The model is optimized for 512 tokens:

• Fast inference speed
• Minimal quality loss (< 1% documents truncated)
• Ideal for most legal document fragments

For longer documents, consider chunking:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Roflmax/bge-m3-legal-ru-cocktail-40-60")
model.max_seq_length = 512

# Split long document into chunks
def chunk_text(text, max_length=2000):
    # Simple character-based chunking
    return [text[i:i+max_length] for i in range(0, len(text), max_length)]

long_document = "Очень длинный документ..."
chunks = chunk_text(long_document)
chunk_embeddings = model.encode(chunks, normalize_embeddings=True)

# Use average embedding for the whole document
import numpy as np
document_embedding = np.mean(chunk_embeddings, ax