Classification : Implement Batch API for model evaluation

Current Behavior
For evaluation of fine-tuned models, we currently use the Responses API synchronously to generate predictions. These predictions are then compared against the true labels of the corresponding questions. However, this synchronous setup is difficult to manage and would require building a complex system to make the whole process asynchronous and more reliable.

Proposed Improvement
Instead of relying on the synchronous approach, we can leverage the OpenAI Batch API. By sending all evaluation queries together as a batch, we can retrieve the predictions from the batch output file and then compare them against the true labels. This would simplify the workflow and make the evaluation process more scalable and efficient.

document to refer : https://docs.google.com/document/d/1y2WLRUm2dZaQMetvb31XpDH5HqteRe4UNpziG8pq608/edit?tab=t.lbfxo8c09bqd#bookmark=id.1ib6z5f31fld

Revisa el flujo actual de evaluación síncrona de la Responses API en el repositorio y, después, consulta el documento de diseño enlazado para conocer el flujo de trabajo por lotes propuesto. Se considera completado cuando las consultas de evaluación se envían mediante la Batch API, las predicciones se leen del archivo de salida del batch y esas predicciones se comparan con las etiquetas verdaderas correspondientes.