code: def test_bert_base_cased():
      spark = SparkSession.builder.master("local[*]").getOrCreate()
  
      train_df, test_df = _prepare_text_data(spark)
  
      ctx = CallbackBackend()
  
      epochs = 2
      callbacks = [
          MyDummyCallback(epochs),
          ModelCheckpoint(dirpath="target/bert_base_uncased/"),
      ]
  
      with local_store() as store:
          checkpoint = "bert-base-uncased"
  
          deep_text_classifier = DeepTextClassifier(
              checkpoint=checkpoint,
              store=store,
              backend=ctx,
              callbacks=callbacks,
              num_classes=6,
              batch_size=16,
              epochs=epochs,
              validation=0.1,
              text_col="Text",
              transformation_removed_fields=[
                  "Text",
                  "Emotion",
                  "label",
              ],
          )
  
          deep_text_model = deep_text_classifier.fit(train_df)
  
          pred_df = deep_text_model.transform(test_df)
          evaluator = MulticlassClassificationEvaluator(
              predictionCol="prediction",
              labelCol="label",
              metricName="accuracy",
          )
          accuracy = evaluator.evaluate(pred_df)
          assert accuracy > 0.5
  
      spark.stop()
  
file_path: SynapseML/deep-learning/src/test/python/synapsemltest/dl/test_deep_text_classifier.py
imports: from pyspark.ml.evaluation import MulticlassClassificationEvaluator
  from pyspark.sql import SparkSession
  from pytorch_lightning.callbacks import ModelCheckpoint
  from synapse.ml.dl import *
  import pytest
  from .conftest import CallbackBackend, _prepare_text_data
  from .test_deep_vision_classifier import local_store
  from .test_deep_vision_model import MyDummyCallback
label_desc: Use image node_id 1 for calling a global function with example usage: test_bert_base_cased() without return types