Modelo BETO Fine-Tuned para Clasificación de Materia Reclamable
Detalles del modelo
Descripción del modelo
Este modelo corresponde a una versión ajustada de BETO, un modelo basado en arquitectura BERT entrenado para procesamiento de lenguaje natural en español. El modelo ha sido fine-tuned para tareas de clasificación automática de materia reclamable en textos relacionados con reclamos de usuarios.
El objetivo principal es automatizar la categorización de documentos y mensajes asociados a procedimientos de atención al usuario, permitiendo mejorar la eficiencia operativa y reducir tiempos de clasificación manual.
- Desarrollado por: [Sergio Luis Parra Alarcon]
- Tipo de modelo: [Transformer Encoder-Only (BERT) para clasificación de texto]
- Arcquitectura base: [BETO uncased Spanish BERT]
- Idioma (PLN): [Español]
- Licencia: [Apache 2.0]
- Framework: [Transformers de Hugging Face]
- Ajustado a partir de: [dccuchile/bert-base-spanish-wwm-uncased]
Fuentes del modelo
- Repositorio: [Pendiente de publicación oficial]
- Framework: [Hugging Face Transformers]
- Modelo base: [BETO Spanish BERT]
Usos
Uso directo
El modelo está diseñado para:
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Evaluation
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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