Continuous Pretraining with 5.4 billion English tokens using knowledge distillation from state-of-the-art reranker models. EuroBERT Foundation building upon the multilingual EuroBERT-210m model specifically designed for European languages. Late Interaction Architecture initialized with PyLate for precise token-level matching and superior retrieval performance.

🎯 Core Features and Innovations:

Continuous Pretraining with Distillation: Enhanced with 5,430,249,475 English tokens while learning from powerful reranker models throughout the training process
Strong Multilingual Foundation: Built on EuroBERT/EuroBERT-210m, which was specifically trained for European language understanding
Full 210M Parameters: Preserving the complete capacity of the base model for maximum multilingual performance

💪 Standing on the Shoulders of Giants

Starting from the exceptional EuroBERT-210m foundation – a model specifically designed for European languages – we've enhanced it with:

5.4 billion additional English tokens through continuous pretraining
Knowledge distillation from state-of-the-art reranker models
Late Interaction architecture for superior retrieval precision

This creates a powerful multilingual retriever that combines the strengths of EuroBERT's native multilingual capabilities with enhanced retrieval performance.

Model Overview

Model: VAGOsolutions/SauerkrautLM-EuroColBERT
Base: Continuous pretrained from EuroBERT/EuroBERT-210m using knowledge distillation
Architecture: PyLate / ColBERT (Late Interaction)
Languages: Multilingual (optimized for 7 European languages: German, English, Spanish, French, Italian, Dutch, Portuguese)
License: Apache 2.0
Model Size: 210M parameters Additional Training: 5.4B English tokens via knowledge distillation

Model Description

Model Type: Multi-vector embedding model with innovative Late Interaction architecture
Document Length: 8192 tokens (32× longer than traditional BERT models)
Query Length: 256 tokens (optimized for complex, multi-part queries)
Output Dimensionality: 128 tokens (efficient vector representation)
Similarity Function: MaxSim (enables precise token-level matching)
Training Method: Continuous pretraining with knowledge distillation

Architecture

ColBERT(
  (0): Transformer(ModernBertModel)
  (1): Dense(768 -> 128 dim, no bias)
)

🔬 Technical Innovations in Detail

Continuous Pretraining with Knowledge Distillation

Our approach enhances the already powerful EuroBERT-210m through:

Base Model Selection: Starting with EuroBERT-210m, which provides exceptional multilingual understanding for European languages
PyLate Initialization: Converting the model to a Late Interaction architecture for superior retrieval capabilities
Massive English Enhancement: 5,430,249,475 English tokens to strengthen English performance
Continuous Distillation: Learning from state-of-the-art reranker models throughout the training process

Architectural Advantages

SauerkrautLM-EuroColBERT leverages:

EuroBERT Foundation: Native multilingual understanding from specialized European language pretraining
Full Parameter Capacity: All 210M parameters preserved for maximum performance
Late Interaction Benefits: Token-level matching for precise retrieval
Enhanced English: Significant improvement in English retrieval through targeted continuous pretraining

This architecture combines the advantages of specialized multilingual pretraining with state-of-the-art retrieval techniques.

🔬 Benchmarks: Multilingual Retrieval Excellence

Our evaluation demonstrates strong multilingual retrieval performance, building upon EuroBERT's foundation with enhanced capabilities through continuous pretraining.

NanoBEIR Europe (multilingual retrieval)

Average nDCG@10 across seven European languages, showing the effectiveness of our continuous pretraining approach:

Language	nDCG@10	Performance Notes
en	58.91	Significantly enhanced through 5.4B token continuous pretraining
de	47.71	Strong german language performance
es	52.15	Excellent spanish language capabilities
fr	50.46	Consistent cross-lingual transfer
it	49.85	Balanced multilingual representation
nl	48.47	Effective on lower-resource languages
pt	50.72	Maintains quality across language families

Key Observations:

Exceptional English Performance: The continuous pretraining with 5.4B English tokens yields outstanding English results (58.91 nDCG@10)
Strong Multilingual Balance: All non-English languages maintain excellent performance (47-52 nDCG@10)
EuroBERT Foundation Benefits: The specialized European language pretraining provides robust cross-lingual capabilities
Distillation Effectiveness: Knowledge distillation from reranker models enhances retrieval quality across all languages

Why SauerkrautLM-EuroColBERT Matters for Production

Best of Both Worlds: Combines EuroBERT's multilingual excellence with enhanced English retrieval
Production Ready: 210M parameters deployable on standard infrastructure
State-of-the-Art Multilingual: Superior performance across 7 European languages
Knowledge Distillation Benefits: Learns from models many times its size
Flexible Foundation: Ideal base for further fine-tuning on specific tasks

This model serves as an excellent foundation for:

Multilingual search systems
Cross-lingual retrieval applications
European language document retrieval
Production systems requiring balanced multilingual performance

Real-World Applications

The combination of EuroBERT's multilingual foundation and our continuous pretraining enables:

Enterprise Search: Deploy across multinational organizations with confidence
E-commerce: Single model for product search across European markets
Digital Libraries: Multilingual document retrieval with high precision
Customer Support: Cross-lingual knowledge base search
Research Applications: Academic and scientific literature retrieval

📈 Summary: Enhanced Multilingual Retrieval

SauerkrautLM-EuroColBERT represents the successful enhancement of an already excellent multilingual model. By combining:

EuroBERT's specialized European language foundation
5.4 billion tokens of continuous pretraining
Knowledge distillation from state-of-the-art rerankers
PyLate's Late Interaction architecture

We've created a model that delivers exceptional multilingual retrieval performance while remaining practical for real-world deployment. The model particularly excels in English (58.91 nDCG@10) while maintaining strong performance across all European languages, making it an ideal choice for multilingual retrieval applications.

Model

This is a multi-vector (ColBERT-style late interaction) embedding model. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Usage

Sentence Transformers

This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the MultiVectorEncoder:

pip install "sentence-transformers>=6.0.0"

python

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("VAGOsolutions/SauerkrautLM-EuroColBERT")

query = "Which planet is known as the Red Planet?"
documents = [
    "Venus wird oft als Zwilling der Erde bezeichnet, wegen ihrer ähnlichen Größe.",
    "Mars, connue pour son apparence rougeâtre, est souvent appelée la planète rouge.",
    "Júpiter es el planeta más grande del sistema solar.",
    "Saturno è famoso per i suoi bellissimi anelli.",
]

query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings[0].shape)
# (32, 128) (21, 128)

# MaxSim late-interaction scoring (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[29.0540, 29.6528, 29.3835, 28.6055]])

PyLate

First install the PyLate library:

pip install -U pylate

Retrieval

PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.

Indexing documents

First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:

python

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model
model = models.ColBERT(
    model_name_or_path="VAGOsolutions/SauerkrautLM-EuroColBERT",
)

# Step 2: Initialize the Voyager index
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

python

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

python

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

python

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="VAGOsolutions/SauerkrautLM-EuroColBERT",
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Citation

BibTeX

SauerkrautLM‑EuroColBERT

bibtex

@misc{SauerkrautLM-EuroColBERT,
  title={SauerkrautLM-EuroColBERT},
  author={David Golchinfar},
  url={https://huggingface.co/VAGOsolutions/SauerkrautLM-EuroColBERT},
  year={2025}
}

EuroBERT-210m

bibtex

@misc{boizard2025eurobertscalingmultilingualencoders,
      title={EuroBERT: Scaling Multilingual Encoders for European Languages}, 
      author={Nicolas Boizard and Hippolyte Gisserot-Boukhlef and Duarte M. Alves and André Martins and Ayoub Hammal and Caio Corro and Céline Hudelot and Emmanuel Malherbe and Etienne Malaboeuf and Fanny Jourdan and Gabriel Hautreux and João Alves and Kevin El-Haddad and Manuel Faysse and Maxime Peyrard and Nuno M. Guerreiro and Patrick Fernandes and Ricardo Rei and Pierre Colombo},
      year={2025},
      eprint={2503.05500},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.05500}, 
}

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
  title = {Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
  author = {Reimers, Nils and Gurevych, Iryna},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
  month = {11},
  year = {2019},
  publisher = {Association for Computational Linguistics},
  url = {https://arxiv.org/abs/1908.10084}
}

PyLate

bibtex

@misc{PyLate,
  title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
  author={Chaffin, Antoine and Sourty, Raphaël},
  url={https://github.com/lightonai/pylate},
  year={2024}
}

Acknowledgements

We thank the PyLate team for providing the training framework that made this work possible, and the EuroBERT team for creating the excellent multilingual foundation model.