diff --git a/spacy/cli/debug_data.py b/spacy/cli/debug_data.py
index b4c420660..3dc8d262d 100644
--- a/spacy/cli/debug_data.py
+++ b/spacy/cli/debug_data.py
@@ -8,12 +8,12 @@ import typer
 
 from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
 from ._util import import_code, debug_cli
-from ..training import Corpus, Example
+from ..training import Example
 from ..training.initialize import get_sourced_components
 from ..schemas import ConfigSchemaTraining
 from ..pipeline._parser_internals import nonproj
 from ..language import Language
-from ..util import registry
+from ..util import registry, resolve_dot_names
 from .. import util
 
 
@@ -37,8 +37,6 @@ BLANK_MODEL_THRESHOLD = 2000
 def debug_data_cli(
     # fmt: off
     ctx: typer.Context,  # This is only used to read additional arguments
-    train_path: Path = Arg(..., help="Location of JSON-formatted training data", exists=True),
-    dev_path: Path = Arg(..., help="Location of JSON-formatted development data", exists=True),
     config_path: Path = Arg(..., help="Path to config file", exists=True),
     code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
     ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
@@ -62,8 +60,6 @@ def debug_data_cli(
     overrides = parse_config_overrides(ctx.args)
     import_code(code_path)
     debug_data(
-        train_path,
-        dev_path,
         config_path,
         config_overrides=overrides,
         ignore_warnings=ignore_warnings,
@@ -74,8 +70,6 @@ def debug_data_cli(
 
 
 def debug_data(
-    train_path: Path,
-    dev_path: Path,
     config_path: Path,
     *,
     config_overrides: Dict[str, Any] = {},
@@ -88,18 +82,11 @@ def debug_data(
         no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
     )
     # Make sure all files and paths exists if they are needed
-    if not train_path.exists():
-        msg.fail("Training data not found", train_path, exits=1)
-    if not dev_path.exists():
-        msg.fail("Development data not found", dev_path, exits=1)
-    if not config_path.exists():
-        msg.fail("Config file not found", config_path, exists=1)
     with show_validation_error(config_path):
         cfg = util.load_config(config_path, overrides=config_overrides)
         nlp = util.load_model_from_config(cfg)
-        T = registry.resolve(
-            nlp.config.interpolate()["training"], schema=ConfigSchemaTraining
-        )
+        config = nlp.config.interpolate()
+        T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
     # Use original config here, not resolved version
     sourced_components = get_sourced_components(cfg)
     frozen_components = T["frozen_components"]
@@ -109,25 +96,15 @@ def debug_data(
     msg.divider("Data file validation")
 
     # Create the gold corpus to be able to better analyze data
-    loading_train_error_message = ""
-    loading_dev_error_message = ""
-    with msg.loading("Loading corpus..."):
-        try:
-            train_dataset = list(Corpus(train_path)(nlp))
-        except ValueError as e:
-            loading_train_error_message = f"Training data cannot be loaded: {e}"
-        try:
-            dev_dataset = list(Corpus(dev_path)(nlp))
-        except ValueError as e:
-            loading_dev_error_message = f"Development data cannot be loaded: {e}"
-    if loading_train_error_message or loading_dev_error_message:
-        if loading_train_error_message:
-            msg.fail(loading_train_error_message)
-        if loading_dev_error_message:
-            msg.fail(loading_dev_error_message)
-        sys.exit(1)
+    dot_names = [T["train_corpus"], T["dev_corpus"]]
+    train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
+    train_dataset = list(train_corpus(nlp))
+    dev_dataset = list(dev_corpus(nlp))
     msg.good("Corpus is loadable")
 
+    nlp.initialize(lambda: train_dataset)
+    msg.good("Pipeline can be initialized with data")
+
     # Create all gold data here to avoid iterating over the train_dataset constantly
     gold_train_data = _compile_gold(train_dataset, factory_names, nlp, make_proj=True)
     gold_train_unpreprocessed_data = _compile_gold(
@@ -348,17 +325,11 @@ def debug_data(
         msg.divider("Part-of-speech Tagging")
         labels = [label for label in gold_train_data["tags"]]
         # TODO: does this need to be updated?
-        tag_map = nlp.vocab.morphology.tag_map
-        msg.info(f"{len(labels)} label(s) in data ({len(tag_map)} label(s) in tag map)")
+        msg.info(f"{len(labels)} label(s) in data")
         labels_with_counts = _format_labels(
             gold_train_data["tags"].most_common(), counts=True
         )
         msg.text(labels_with_counts, show=verbose)
-        non_tagmap = [l for l in labels if l not in tag_map]
-        if not non_tagmap:
-            msg.good(f"All labels present in tag map for language '{nlp.lang}'")
-        for label in non_tagmap:
-            msg.fail(f"Label '{label}' not found in tag map for language '{nlp.lang}'")
 
     if "parser" in factory_names:
         has_low_data_warning = False
diff --git a/spacy/cli/templates/quickstart_training.jinja b/spacy/cli/templates/quickstart_training.jinja
index efe19d315..adad72995 100644
--- a/spacy/cli/templates/quickstart_training.jinja
+++ b/spacy/cli/templates/quickstart_training.jinja
@@ -270,7 +270,6 @@ factory = "{{ pipe }}"
 @readers = "spacy.Corpus.v1"
 path = ${paths.train}
 max_length = {{ 500 if hardware == "gpu" else 2000 }}
-augmenter = {"@augmenters": "spacy.orth_variants.v1", "level": 0.1, "lower": 0.5}
 
 [corpora.dev]
 @readers = "spacy.Corpus.v1"
diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md
index 5c9f7e480..66e26f11f 100644
--- a/website/docs/api/cli.md
+++ b/website/docs/api/cli.md
@@ -436,6 +436,7 @@ $ python -m spacy debug data [config_path] [--code] [--ignore-warnings] [--verbo
 ```
 =========================== Data format validation ===========================
 ✔ Corpus is loadable
+✔ Pipeline can be initialized with data
 
 =============================== Training stats ===============================
 Training pipeline: tagger, parser, ner
@@ -465,7 +466,7 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
 ✔ No entities consisting of or starting/ending with whitespace
 
 =========================== Part-of-speech Tagging ===========================
-ℹ 49 labels in data (57 labels in tag map)
+ℹ 49 labels in data
 'NN' (266331), 'IN' (227365), 'DT' (185600), 'NNP' (164404), 'JJ' (119830),
 'NNS' (110957), '.' (101482), ',' (92476), 'RB' (90090), 'PRP' (90081), 'VB'
 (74538), 'VBD' (68199), 'CC' (62862), 'VBZ' (50712), 'VBP' (43420), 'VBN'
@@ -476,7 +477,6 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
 '-RRB-' (2825), '-LRB-' (2788), 'PDT' (2078), 'XX' (1316), 'RBS' (1142), 'FW'
 (794), 'NFP' (557), 'SYM' (440), 'WP$' (294), 'LS' (293), 'ADD' (191), 'AFX'
 (24)
-✔ All labels present in tag map for language 'en'
 
 ============================= Dependency Parsing =============================
 ℹ Found 111703 sentences with an average length of 18.6 words.