Fine-Tuning de Transformers para Datos Empresariales

How to train a model for sentiment analysis?

Sentiment analysis is a powerful tool for understanding opinions expressed in texts. Let's explore how to train a custom model to classify Spanish reviews as positive or negative.

Preparing our data

To begin this process, we need to properly structure our data. The first step is to import a dataset of reviews and prepare it for training:

import pandas as pddf = pd.read_csv('review_dataset.csv')

Once the dataset is loaded, we can verify that it contains the necessary information: the review text (review_body) and the rating (stars). It is essential to condition this data by dividing the dataset into three parts:

• 70% for training
• 20% for testing
• 10% for validation

Transforming the data for the model

To work with natural language processing models, we need to convert our ratings into binary labels:

• Reviews with more than 3 stars → positive
• Reviews with 3 or less stars → negative

This step is crucial so that the model can learn to distinguish between positive and negative sentiments in the text.

Using a pre-trained model in Spanish.

For best results, we will use a specialized Spanish base model, such as RoBERTa-BNE, trained with the corpus of the National Library of Spain. This transfer learning approach allows us to take advantage of a model that already understands the structure of the Spanish language.

Tokenization is an essential step in this process:

# we tokenize exclusively the content of the reviewstokenized_data = tokenizer(df['review_body'].tolist())

Training the model

The fine-tuning process allows us to adapt the pre-trained model to our specific binary classification task. Key parameters include:

• Batch size: 8 (suitable for Google Colab).
• Epochs: 2 (sufficient for this well-balanced dataset)
• Learning rate: default values

Importantly, once trained, the model can be uploaded to Hugging Face Hub, allowing anyone in the world to use it.

Evaluating performance

After training, we can test our model with real examples:

• "I loved the pants" → Positive (99% confidence).
• "It forces you to buy two units" → Negative (94% confidence).
• "Worst purchase of my life, do not recommend" → Negative (99% confidence).

This type of personalized sentiment analysis has immediate practical applications for any business that receives customer feedback.

Have you ever trained a natural language processing model? Share your experience or doubts in the comments and don't forget to test this approach with your own data.