Simplify lemma data preparation & usage - no need for both an 'in' an…

…d a 'gold' file, at least not at present

stanza/models/lemma/scorer.py

@@ -2,10 +2,15 @@
 Utils and wrappers for scoring lemmatizers.
 """
 
+import logging
+
 from stanza.models.common.utils import ud_scores
 
+logger = logging.getLogger('stanza')
+
 def score(system_conllu_file, gold_conllu_file):
     """ Wrapper for lemma scorer. """
+    logger.debug("Evaluating system file %s vs gold file %s", system_conllu_file, gold_conllu_file)
     evaluation = ud_scores(gold_conllu_file, system_conllu_file)
     el = evaluation["Lemmas"]
     p, r, f = el.precision, el.recall, el.f1

stanza/models/lemmatizer.py

@@ -33,10 +33,9 @@
 def build_argparse():
     parser = argparse.ArgumentParser()
     parser.add_argument('--data_dir', type=str, default='data/lemma', help='Directory for all lemma data.')
-    parser.add_argument('--train_file', type=str, default=None, help='Input file for data loader.')
-    parser.add_argument('--eval_file', type=str, default=None, help='Input file for data loader.')
+    parser.add_argument('--train_file', type=str, default=None, help='Training input file for data loader.')
+    parser.add_argument('--eval_file', type=str, default=None, help='Evaluation input file for data loader.')
     parser.add_argument('--output_file', type=str, default=None, help='Output CoNLL-U file.')
-    parser.add_argument('--gold_file', type=str, default=None, help='Output CoNLL-U file.')
 
     parser.add_argument('--mode', default='train', choices=['train', 'predict'])
     parser.add_argument('--shorthand', type=str, help='Shorthand for the dataset to use.  lang_dataset')
@@ -147,7 +146,7 @@ def train(args):
 
     # pred and gold path
     system_pred_file = args['output_file']
-    gold_file = args['gold_file']
+    gold_file = args['eval_file']
 
     utils.print_config(args)
 
@@ -258,7 +257,6 @@ def train(args):
 def evaluate(args):
     # file paths
     system_pred_file = args['output_file']
-    gold_file = args['gold_file']
     model_file = build_model_filename(args)
 
     # load model
@@ -304,10 +302,9 @@ def evaluate(args):
     # write to file and score
     batch.doc.set([LEMMA], preds)
     CoNLL.write_doc2conll(batch.doc, system_pred_file)
-    if gold_file is not None:
-        _, _, score = scorer.score(system_pred_file, gold_file)
 
-        logger.info("Finished evaluation\nLemma score:\n{} {:.2f}".format(args['shorthand'], score*100))
+    _, _, score = scorer.score(system_pred_file, args['eval_file'])
+    logger.info("Finished evaluation\nLemma score:\n{} {:.2f}".format(args['shorthand'], score*100))
 
 if __name__ == '__main__':
     main()

stanza/utils/datasets/prepare_tokenizer_treebank.py

@@ -82,9 +82,6 @@ def copy_conllu_treebank(treebank, model_type, paths, dest_dir, postprocess=None
         postprocess(tokenizer_dir, "train.gold", dest_dir, "train.in", short_name)
         postprocess(tokenizer_dir, "dev.gold", dest_dir, "dev.in", short_name)
         postprocess(tokenizer_dir, "test.gold", dest_dir, "test.in", short_name)
-        if model_type is not common.ModelType.POS and model_type is not common.ModelType.DEPPARSE:
-            copy_conllu_file(dest_dir, "dev.in", dest_dir, "dev.gold", short_name)
-            copy_conllu_file(dest_dir, "test.in", dest_dir, "test.gold", short_name)
 
 def split_train_file(treebank, train_input_conllu, train_output_conllu, dev_output_conllu):
     # set the seed for each data file so that the results are the same

stanza/utils/training/run_lemma.py

@@ -79,10 +79,8 @@ def run_treebank(mode, paths, treebank, short_name,
     lemma_dir      = paths["LEMMA_DATA_DIR"]
     train_file     = f"{lemma_dir}/{short_name}.train.in.conllu"
     dev_in_file    = f"{lemma_dir}/{short_name}.dev.in.conllu"
-    dev_gold_file  = f"{lemma_dir}/{short_name}.dev.gold.conllu"
     dev_pred_file  = temp_output_file if temp_output_file else f"{lemma_dir}/{short_name}.dev.pred.conllu"
     test_in_file   = f"{lemma_dir}/{short_name}.test.in.conllu"
-    test_gold_file = f"{lemma_dir}/{short_name}.test.gold.conllu"
     test_pred_file = temp_output_file if temp_output_file else f"{lemma_dir}/{short_name}.test.pred.conllu"
 
     charlm_args = build_lemma_charlm_args(short_language, dataset, command_args.charlm)
@@ -99,15 +97,13 @@ def run_treebank(mode, paths, treebank, short_name,
             train_args = ["--train_file", train_file,
                           "--eval_file", dev_in_file,
                           "--output_file", dev_pred_file,
-                          "--gold_file", dev_gold_file,
                           "--shorthand", short_name]
             logger.info("Running identity lemmatizer for {} with args {}".format(treebank, train_args))
             identity_lemmatizer.main(train_args)
         elif mode == Mode.SCORE_TEST:
             train_args = ["--train_file", train_file,
                           "--eval_file", test_in_file,
                           "--output_file", test_pred_file,
-                          "--gold_file", test_gold_file,
                           "--shorthand", short_name]
             logger.info("Running identity lemmatizer for {} with args {}".format(treebank, train_args))
             identity_lemmatizer.main(train_args)            
@@ -122,7 +118,6 @@ def run_treebank(mode, paths, treebank, short_name,
             train_args = ["--train_file", train_file,
                           "--eval_file", dev_in_file,
                           "--output_file", dev_pred_file,
-                          "--gold_file", dev_gold_file,
                           "--shorthand", short_name,
                           "--num_epoch", num_epochs,
                           "--mode", "train"]
@@ -133,7 +128,6 @@ def run_treebank(mode, paths, treebank, short_name,
         if mode == Mode.SCORE_DEV or mode == Mode.TRAIN:
             dev_args = ["--eval_file", dev_in_file,
                         "--output_file", dev_pred_file,
-                        "--gold_file", dev_gold_file,
                         "--shorthand", short_name,
                         "--mode", "predict"]
             dev_args = dev_args + charlm_args + extra_args
@@ -143,7 +137,6 @@ def run_treebank(mode, paths, treebank, short_name,
         if mode == Mode.SCORE_TEST:
             test_args = ["--eval_file", test_in_file,
                          "--output_file", test_pred_file,
-                         "--gold_file", test_gold_file,
                          "--shorthand", short_name,
                          "--mode", "predict"]
             test_args = test_args + charlm_args + extra_args