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| import pipes | |
| class ModelManager: | |
| def __init__(self): | |
| self.models = {} # Un diccionario para almacenar los modelos disponibles | |
| def list_models(self): | |
| return list(self.models.keys()) | |
| def add_model(self, pipe_func, model_name, args): | |
| self.models[model_name] = {"pipeline": pipe_func, "args": args} | |
| def load_transformers_model(self, model_name, args): | |
| if hasattr(pipes, model_name): | |
| pipe_func = getattr(pipes, model_name) | |
| self.add_model(pipe_func, model_name, args) | |
| else: | |
| print(f"Error: {model_name} no está definido en el módulo pipes.") | |
| def train_transformers_model(self, model_name, train_dataset, eval_dataset, training_args): | |
| if model_name not in self.models: | |
| print(f"Error: {model_name} no está en la lista de modelos disponibles.") | |
| return | |
| pipeline = self.models[model_name]["pipeline"] | |
| pipeline.train(train_dataset=train_dataset, eval_dataset=eval_dataset, training_args=training_args) | |
| def test_model(self, model_name, test_dataset): | |
| if model_name not in self.models: | |
| print(f"Error: {model_name} no está en la lista de modelos disponibles.") | |
| return | |
| pipeline = self.models[model_name]["pipeline"] | |
| return pipeline.test(test_dataset) | |
| def remove_model(self, model_name): | |
| if model_name in self.models: | |
| del self.models[model_name] | |
| else: | |
| print(f"Error: {model_name} no está en la lista de modelos disponibles.") | |
| def execute_model(self, model_name, *args, **kwargs): | |
| if model_name not in self.models: | |
| print(f"Error: {model_name} no está en la lista de modelos disponibles.") | |
| return None | |
| pipe_func = self.models[model_name]["pipeline"] | |
| args = self.models[model_name]["args"] | |
| return pipe_func(*args, **kwargs) | |
| def choose_best_pipeline(self, prompt, task): | |
| available_pipelines = self.models.keys() | |
| best_pipeline = None | |
| best_score = float('-inf') | |
| for pipeline_name in available_pipelines: | |
| pipeline = self.models[pipeline_name]["pipeline"] | |
| score = self.evaluate_pipeline(pipeline, prompt, task) | |
| if score > best_score: | |
| best_score = score | |
| best_pipeline = pipeline_name | |
| return best_pipeline | |
| def evaluate_pipeline(self, pipeline, prompt, task): | |
| # Aquí puedes implementar la lógica para evaluar qué pipeline es mejor para la tarea específica | |
| # En este ejemplo, utilizamos la métrica de exactitud para el análisis de sentimiento | |
| if task == "sentiment_analysis": | |
| # Supongamos que test_dataset contiene pares de (texto, etiqueta) para análisis de sentimiento | |
| test_dataset = [("Texto de prueba 1", "positivo"), ("Texto de prueba 2", "negativo")] | |
| correct_predictions = 0 | |
| total_predictions = len(test_dataset) | |
| for text, label in test_dataset: | |
| prediction = pipeline(text) | |
| if prediction == label: | |
| correct_predictions += 1 | |
| accuracy = correct_predictions / total_predictions | |
| return accuracy | |
| else: | |
| # Implementa la lógica de evaluación para otras tareas aquí | |
| return 0.5 # Por ahora, retornamos un valor de evaluación arbitrario | |
| # Ejemplo de uso | |
| if __name__ == "__main__": | |
| manager = ModelManager() | |
| # Añadir pipelines | |
| manager.load_transformers_model("sentiment_tags", args={}) | |
| manager.load_transformers_model("entity_pos_tagger", args={}) | |
| # Decidir qué pipeline usar para el análisis de sentimiento | |
| prompt = "Este es un texto de ejemplo para analizar el sentimiento." | |
| task = "sentiment_analysis" | |
| best_pipeline = manager.choose_best_pipeline(prompt, task) | |
| print(f"La mejor pipa para {task} es: {best_pipeline}") | |