trained data specified in function call

src/python/example_train-ner.py

@@ -27,14 +27,15 @@ from utils_proc import call_with_out
     model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
     output_dir=("Optional output directory", "option", "o", Path),
     n_iter=("Number of training iterations", "option", "n", int),
-    train_data=("Training data. So far document-wise.", "option", "t", Path),
+    training_data=("Training data. So far document-wise.", "option", "t", Path),
+    trained_data=("Trained data. Model generated from training run on training data.", "option", "u", Path),
     print_output=("Print output. Boolean.", "option", "p", bool)
 )
-def main(model=None, output_dir=None, n_iter=100, train_data=None, print_output=False):
+def main(model=None, output_dir=None, n_iter=100, training_data=None, trained_data=None, print_output=False):
     """Load training data and the model, set up the pipeline and train the entity recognizer."""
-    if train_data is not None:
-        call_with_out("git-lfs pull -I " + train_data.as_posix())
-        dict_onedoc = read_from_txt(train_data)
+    if training_data is not None:
+        call_with_out("git-lfs pull -I " + training_data.as_posix())
+        dict_onedoc = read_from_txt(training_data)
         TRAIN_DATA = transform_to_training_format(dict_onedoc)[:50]   # TODO: get rid of [:50]
         TRAIN_DATA_orig = TRAIN_DATA[:]
         print(type(TRAIN_DATA), TRAIN_DATA[:10])
@@ -107,8 +108,8 @@ def main(model=None, output_dir=None, n_iter=100, train_data=None, print_output=
 
     alldicts_tested = transform_to_reading_format(TRAIN_DATA_tested)
 
-    filename_tested = str(train_data)[:-4] + '_trained.txt'
-    write_to_txt(alldicts_tested, filename_tested)
+    if trained_data is not None:
+        write_to_txt(alldicts_tested, trained_data)
 
     # save model to output directory
     if output_dir is not None: