Add tag_map argument to CLI debug-data and train (#4750)

Add an argument for a path to a JSON-formatted tag map, which is used to
update and extend the default language tag map.

spacy/cli/debug_data.py

@@ -26,6 +26,7 @@ BLANK_MODEL_THRESHOLD = 2000
     lang=("model language", "positional", None, str),
     train_path=("location of JSON-formatted training data", "positional", None, Path),
     dev_path=("location of JSON-formatted development data", "positional", None, Path),
+    tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
     base_model=("name of model to update (optional)", "option", "b", str),
     pipeline=(
         "Comma-separated names of pipeline components to train",
@@ -41,6 +42,7 @@ def debug_data(
     lang,
     train_path,
     dev_path,
+    tag_map_path=None,
     base_model=None,
     pipeline="tagger,parser,ner",
     ignore_warnings=False,
@@ -60,6 +62,10 @@ def debug_data(
     if not dev_path.exists():
         msg.fail("Development data not found", dev_path, exits=1)
 
+    tag_map = {}
+    if tag_map_path is not None:
+        tag_map = srsly.read_json(tag_map_path)
+
     # Initialize the model and pipeline
     pipeline = [p.strip() for p in pipeline.split(",")]
     if base_model:
@@ -67,6 +73,8 @@ def debug_data(
     else:
         lang_cls = get_lang_class(lang)
         nlp = lang_cls()
+    # Update tag map with provided mapping
+    nlp.vocab.morphology.tag_map.update(tag_map)
 
     msg.divider("Data format validation")
 
@@ -329,7 +337,7 @@ def debug_data(
     if "tagger" in pipeline:
         msg.divider("Part-of-speech Tagging")
         labels = [label for label in gold_train_data["tags"]]
-        tag_map = nlp.Defaults.tag_map
+        tag_map = nlp.vocab.morphology.tag_map
         msg.info(
             "{} {} in data ({} {} in tag map)".format(
                 len(labels),

spacy/cli/train.py

@@ -48,6 +48,7 @@ from .. import about
     textcat_multilabel=("Textcat classes aren't mutually exclusive (multilabel)", "flag", "TML", bool),
     textcat_arch=("Textcat model architecture", "option", "ta", str),
     textcat_positive_label=("Textcat positive label for binary classes with two labels", "option", "tpl", str),
+    tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
     verbose=("Display more information for debug", "flag", "VV", bool),
     debug=("Run data diagnostics before training", "flag", "D", bool),
     # fmt: on
@@ -78,6 +79,7 @@ def train(
     textcat_multilabel=False,
     textcat_arch="bow",
     textcat_positive_label=None,
+    tag_map_path=None,
     verbose=False,
     debug=False,
 ):
@@ -118,6 +120,9 @@ def train(
     if not output_path.exists():
         output_path.mkdir()
 
+    tag_map = {}
+    if tag_map_path is not None:
+        tag_map = srsly.read_json(tag_map_path)
     # Take dropout and batch size as generators of values -- dropout
     # starts high and decays sharply, to force the optimizer to explore.
     # Batch size starts at 1 and grows, so that we make updates quickly
@@ -209,6 +214,9 @@ def train(
                 pipe_cfg = {}
             nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
 
+    # Update tag map with provided mapping
+    nlp.vocab.morphology.tag_map.update(tag_map)
+
     if vectors:
         msg.text("Loading vector from model '{}'".format(vectors))
         _load_vectors(nlp, vectors)