Make tensorboard metrics lighter, group output file config flags.

combo/data/dataset.py

@@ -46,6 +46,8 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
         fields = list(parser.DEFAULT_FIELDS)
         fields[1] = 'token'  # use 'token' instead of 'form'
         field_parsers = parser.DEFAULT_FIELD_PARSERS
+        # Do not make it nullable
+        field_parsers.pop('xpostag', None)
         if self._use_sem:
             fields = list(fields)
             fields.append('semrel')

combo/main.py

@@ -25,6 +25,8 @@ flags.DEFINE_enum(name='mode', default=None, enum_values=['train', 'predict'],
 # Common flags
 flags.DEFINE_integer(name='cuda_device', default=-1,
                      help="Cuda device id (default -1 cpu)")
+flags.DEFINE_string(name='output_file', default='output.log',
+                    help='Predictions result file.')
 
 # Training flags
 flags.DEFINE_list(name='training_data_path', default="./tests/fixtures/example.conllu",
@@ -60,8 +62,6 @@ flags.DEFINE_string(name='config_path', default='config.template.jsonnet',
                     help='Config file path.')
 
 # Test after training flags
-flags.DEFINE_string(name='result', default='result.conllu',
-                    help='Test result path file')
 flags.DEFINE_string(name='test_path', default=None,
                     help='Test path file.')
 
@@ -74,8 +74,6 @@ flags.DEFINE_string(name='model_path', default=None,
                     help='Pretrained model path.')
 flags.DEFINE_string(name='input_file', default=None,
                     help='File to predict path')
-flags.DEFINE_string(name='output_file', default='output.log',
-                    help='Predictions result file.')
 flags.DEFINE_integer(name='batch_size', default=1,
                      help='Prediction batch size.')
 flags.DEFINE_boolean(name='silent', default=True,
@@ -126,7 +124,7 @@ def run(_):
 
             logger.info(f'Finetuned model stored in: {serialization_dir}')
 
-        if FLAGS.test_path and FLAGS.result:
+        if FLAGS.test_path and FLAGS.output_file:
             checks.file_exists(FLAGS.test_path)
             params = common.Params.from_file(FLAGS.config_path, ext_vars=_get_ext_vars())['dataset_reader']
             params.pop('type')
@@ -137,7 +135,7 @@ def run(_):
             )
             test_path = FLAGS.test_path
             test_trees = dataset_reader.read(test_path)
-            with open(FLAGS.result, 'w') as f:
+            with open(FLAGS.output_file, 'w') as f:
                 for tree in test_trees:
                     f.writelines(predictor.predict_instance_as_tree(tree).serialize())
     else:

combo/models/base.py

@@ -102,7 +102,8 @@ class FeedForwardPredictor(Predictor):
                 "len(hidden_dims) (%d) + 1 != num_layers (%d)" % (len(hidden_dims), num_layers)
             )
 
-        assert vocab_namespace in vocab.get_namespaces()
+        assert vocab_namespace in vocab.get_namespaces(),\
+            f"There is not {vocab_namespace} in created vocabs, check if this field has any values to predict!"
         hidden_dims = hidden_dims + [vocab.get_vocab_size(vocab_namespace)]
 
         return cls(feedforward.FeedForward(

config.template.jsonnet

@@ -364,8 +364,9 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
         },
         tensorboard_writer: {
             serialization_dir: metrics_dir,
-            should_log_learning_rate: true,
-            summary_interval: 2,
+            should_log_learning_rate: false,
+            should_log_parameter_statistics: false,
+            summary_interval: 100,
         },
         validation_metric: "+EM",
     },