---
tags:
- ColBERT
- PyLate
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:909188
- loss:Contrastive
base_model: EuroBERT/EuroBERT-610m
datasets:
- baconnier/rag-comprehensive-triplets
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- accuracy
model-index:
- name: PyLate model based on EuroBERT/EuroBERT-610m
  results:
  - task:
      type: col-berttriplet
      name: Col BERTTriplet
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: accuracy
      value: 0.9841766953468323
      name: Accuracy
license: apache-2.0
language:
- es
- en
---

## Fine-Tuned Model

**`raialvaro/colbert-610M-EuroBERT`**

## Base Model
**`EuroBERT/EuroBERT-610m`**

## Fine-Tuning Method
Fine-tuning was performed using **[PyLate](https://github.com/lightonai/pylate)**, with contrastive training on the [rag-comprehensive-triplets](https://huggingface.co/datasets/baconnier/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`](https://huggingface.co/datasets/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>

| Metric       | Value      |
|:-------------|:-----------|
| **accuracy** | **0.98417** |

## Usage
First install the PyLate library:

```bash
pip install -U pylate
```

### Calculate Similarity

```python
import torch
from pylate import models

# Load the ColBERT model 
model = models.ColBERT("raialvaro/colbert-610M-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:** raialvaro
- **License:** apache-2.0