added separate validation batches per epoch

combo/main.py

@@ -63,6 +63,8 @@ flags.DEFINE_integer(name="batch_size", default=256,
                      help="Batch size")
 flags.DEFINE_integer(name="batches_per_epoch", default=16,
                      help="Number of batches per epoch")
+flags.DEFINE_integer(name="validation_batches_per_epoch", default=4,
+                     help="Number of batches per epoch")
 flags.DEFINE_string(name="pretrained_transformer_name", default="bert-base-cased",
                     help="Pretrained transformer model name (see transformers from HuggingFace library for list of "
                          "available models) for transformers based embeddings.")
@@ -171,7 +173,7 @@ def get_defaults(dataset_reader: Optional[DatasetReader],
                            str(training_data_loader.data_path), prefix=prefix)
 
     if FLAGS.validation_data_path and not validation_data_loader:
-        validation_data_loader = default_data_loader(dataset_reader, validation_data_path, FLAGS.batch_size, FLAGS.batches_per_epoch)
+        validation_data_loader = default_data_loader(dataset_reader, validation_data_path, FLAGS.batch_size, FLAGS.validation_batches_per_epoch)
     elif FLAGS.validation_data_path and validation_data_loader:
         if validation_data_path:
             validation_data_loader.data_path = validation_data_path
@@ -514,7 +516,7 @@ def _get_ext_vars(finetuning: bool = False) -> Dict:
             "parameters": {
                 "data_path": FLAGS.validation_data_path if not finetuning else FLAGS.finetuning_validation_data_path,
                 "batch_size": FLAGS.batch_size,
-                "batches_per_epoch": FLAGS.batches_per_epoch,
+                "batches_per_epoch": FLAGS.validation_batches_per_epoch,
                 "reader": {
                     "parameters": {
                         "features": FLAGS.features,