col1-210M-EuroBERT

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Fine-Tuned Model

fjmgAI/col1-210M-EuroBERT

Base Model

EuroBERT/EuroBERT-210m

Fine-Tuning Method

Fine-tuning was performed using PyLate, with contrastive training on the rag-comprehensive-triplets dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Dataset

baconnier/rag-comprehensive-triplets

Description

This dataset has been filtered for the Spanish language containing 303,000 examples, designed for rag-comprehensive-triplets.

Fine-Tuning Details

• The model was trained using the Contrastive Training.
• Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>

Usage

First install the PyLate library:

pip install -U pylate

Calculate Similarity

import torch
from pylate import models

# Load the ColBERT model 
model = models.ColBERT("fjmgAI/col1-210M-EuroBERT", trust_remote_code=True)

# Move the model to GPU if available, otherwise use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Example data for similarity comparison
query = "¿Cuál es la capital de España?"  # Query sentence
positive_doc = "La capital de España es Madrid."  # Relevant document
negative_doc = "Florida es un estado en los Estados Unidos."  # Irrelevant document
sentences = [query, positive_doc, negative_doc]  # Combine all texts

# Tokenize the input sentences using ColBERT's tokenizer
inputs = model.tokenize(sentences)

# Move all input tensors to the same device as the model (GPU/CPU)
inputs = {key: value.to(device) for key, value in inputs.items()}

# Generate token embeddings (no gradients needed for inference)
with torch.no_grad():
    embeddings_dict = model(inputs)  
    embeddings = embeddings_dict['token_embeddings']

# Define ColBERT's MaxSim similarity function
def colbert_similarity(query_emb, doc_emb):
    """
    Computes ColBERT-style similarity between query and document embeddings.
    Uses maximum similarity (MaxSim) between individual tokens.
    
    Args:
        query_emb: [query_tokens, embedding_dim]
        doc_emb: [doc_tokens, embedding_dim]
    
    Returns:
        Normalized similarity score
    """
    # Compute dot product between all token pairs
    similarity_matrix = torch.matmul(query_emb, doc_emb.T)  
    
    # Get maximum similarity for each query token (MaxSim)
    max_similarities = similarity_matrix.max(dim=1)[0]
    
    # Return average of maximum similarities (normalized by query length)
    return max_similarities.sum() / query_emb.shape[0]

# Extract embeddings for each text
query_emb = embeddings[0]  
positive_emb = embeddings[1]  
negative_emb = embeddings[2]

# Compute similarity scores
positive_score = colbert_similarity(query_emb, positive_emb)
negative_score = colbert_similarity(query_emb, negative_emb)

print(f"Similarity with positive document: {positive_score.item():.4f}")
print(f"Similarity with negative document: {negative_score.item():.4f}")

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.4.1
• PyLate: 1.1.7
• Transformers: 4.48.2
• PyTorch: 2.5.1+cu121
• Accelerate: 1.2.1
• Datasets: 3.3.1
• Tokenizers: 0.21.0

Purpose

This tuned model is designed for Spanish applications that require the use of efficient semantic search comparing embeddings at the token level with its MaxSim operation, ideal for question-answering and document retrieval.

• Developed by: fjmgAI
• License: apache-2.0

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