Batch processing for stream dataset
Hello,I am working on the Oscar dataset and trying to filter some entries based on the results of the One-Shot classification model.Since the Oscars dataset is huge and I can’t load at once, I’ m using the stream mode of Datasets.To filter the dataset, I use this functiondef infernce_batch(examples):

   outputs, scores, texts   = [], [], []

   for exemple in  exemples['text']:
       res = classifier_ort(exemple, classes)
       res_mean = np.mean(np.array(res['scores']))

       outputs.append(True if res_mean > 0.5 else False)
       scores.append(res_mean)
       texts.append(exemple)

   return {"text": texts, "is_class": outputs, "score": scores}After that I call theMAPfunction to iterate throw the datasetupdated_dataset = oscar_dataset_streamed.map(infernce_batch, 
                                  batched=True, 
                                  batch_size=1000)And to get back the resultsfor exemple in updated_dataset.take(100):

    print(exemple['id'])
    print(exemple['text'])To me, this is not the most efficient way to process this dataset, as it is streaming data row by row and not by batch, and I didn’t see any difference between using theMapfunction withbatched=Trueand theMAPfunction withbatched=False.So my question is, is there an efficient way to process the Oscar dataset faster using stream mode and batch processing?Thank you