Model Card: Spanish Text Reranker using BETO

This model is a reranker for Spanish text passages, built on top of BETO (a BERT-based model pre-trained on Spanish). It was trained to score the relevance of text passages given a user prompt, enabling you to reorder search results or candidate answers by how closely they match the user’s query.

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Model Details

• Model Name: reranker_beto_pytorch_optimized
• Architecture: BETO (BERT-base Spanish WWM)
• Language: Spanish
• Task: Regression-based Reranking
  • Given a (prompt, content) pair, the model outputs a single numerical score indicating predicted relevance.

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Intended Use and Applications

1. Passage Reranking: Use the model to rerank search results, QA passages, or any candidate text snippet according to how well they answer a Spanish query.
2. Information Retrieval Pipelines: Integrate the model as a final step after retrieving multiple candidate passages from a search engine. The model will reorder candidates by relevance.
3. Question-Answering Systems: Filter or sort passages that might contain the best answer to a user’s Spanish question.

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How It Was Trained

1. Data Source:

   • Model training data came from an internal system that stores:
     • Prompts (user queries or questions)
     • Content (text chunks from documents)
     • Rank (a manual or heuristic-based 1–5 relevance score)
   • Additional generation steps (HyDE / T5) were used to create synthetic queries, but this reranker model specifically used the (prompt, content, rank) tuples from the database.

2. Preprocessing:

   • The textual pairs (prompt, content) were tokenized using the BETO tokenizer (cased) with:
     • max_length = 512
     • doc_stride = 256 (for lengthy passages)
   • The rank field was normalized and mapped to a continuous value (relevance) for regression.

3. Training Setup:

   • Base model: dccuchile/bert-base-spanish-wwm-cased
   • Loss: Mean Squared Error (MSE) to predict the relevance score
   • Optimizer: AdamW with a learning rate of 3e-5
   • Epochs: 3
   • Batch Size: 8
   • Hardware: CPU/GPU (CUDA if available)

4. Splits:

   • Data was split into train (80%), validation (10%), and test (10%) sets using sklearn.model_selection.train_test_split.

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Model Performance

• The code logs training and validation loss (MSE).
• Final test set MSE is logged as test_loss.
• Specific numerical results depend on your data distribution and training logs.

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Usage Example

Below is a quick example in Python using Hugging Face Transformers. After you’ve downloaded the model and tokenizer to ./reranker_beto_pytorch_optimized, you can do:

import torch
from transformers import BertTokenizer, BertForSequenceClassification

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the fine-tuned model and tokenizer
model_dir = "./reranker_beto_pytorch_optimized"
tokenizer = BertTokenizer.from_pretrained(model_dir)
model = BertForSequenceClassification.from_pretrained(model_dir).to(device)
model.eval()

prompt = "¿Cómo implementar un sistema solar en una escuela primaria?"
passage = "Este documento describe las partes del sistema solar ..."

inputs = tokenizer(
    prompt,
    passage,
    max_length=512,
    truncation='only_second', 
    padding='max_length',
    return_tensors='pt'
)

# Forward pass
with torch.no_grad():
    outputs = model(
        input_ids=inputs['input_ids'].to(device),
        attention_mask=inputs['attention_mask'].to(device)
    )
score = outputs.logits.squeeze().item()

print(f"Predicted relevance score: {score:.4f}")

You would compare scores across multiple passages for a single prompt, then rank or sort them from highest to lowest predicted relevance.

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Limitations and Ethical Considerations

1. Bias and Fairness:
   • Model performance is influenced by training data’s content and labels. If the data distribution is skewed, the model might reflect those biases (e.g., domain-specific content, reading level bias).
2. Domain Generalization:
   • Trained primarily on text from a specific database of Spanish prompts and passages. Performance may degrade in highly specialized or different domains, or with non-standard Spanish dialects.
3. Possible Misinformation:
   • Reranking aims to find the “most relevant” snippet, not necessarily the “most correct” or “fact-checked.” Always verify final results for correctness or harmful misinformation.
4. Data Confidentiality:
   • If your data contains personal or sensitive info, ensure you comply with relevant privacy and data handling regulations before using or distributing the model.

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Intended Users

• Developers building Spanish-based search and question-answering systems.
• Researchers experimenting with Spanish language reranking or IR tasks.
• Content Managers wanting to reorder Spanish text snippets by relevance.

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