cleanup and formatting

spacy/cli/pretrain.py

@@ -71,9 +71,7 @@ def pretrain_cli(
 
     with show_validation_error(config_path):
         config = util.load_config(
-            config_path,
-            overrides=config_overrides,
-            interpolate=True
+            config_path, overrides=config_overrides, interpolate=True
         )
     if not config.get("pretraining"):
         # TODO: What's the solution here? How do we handle optional blocks?
@@ -84,7 +82,7 @@ def pretrain_cli(
 
     config.to_disk(output_dir / "config.cfg")
     msg.good("Saved config file in the output directory")
- 
+
     pretrain(
         config,
         output_dir,
@@ -99,7 +97,7 @@ def pretrain(
     output_dir: Path,
     resume_path: Optional[Path] = None,
     epoch_resume: Optional[int] = None,
-    use_gpu: int=-1
+    use_gpu: int = -1,
 ):
     if config["system"].get("seed") is not None:
         fix_random_seed(config["system"]["seed"])
@@ -107,7 +105,7 @@ def pretrain(
         use_pytorch_for_gpu_memory()
     nlp, config = util.load_model_from_config(config)
     P_cfg = config["pretraining"]
-    corpus = dot_to_object(config, config["pretraining"]["corpus"])
+    corpus = dot_to_object(config, P_cfg["corpus"])
     batcher = P_cfg["batcher"]
     model = create_pretraining_model(nlp, config["pretraining"])
     optimizer = config["pretraining"]["optimizer"]
@@ -148,9 +146,7 @@ def pretrain(
             progress = tracker.update(epoch, loss, docs)
             if progress:
                 msg.row(progress, **row_settings)
-            if P_cfg["n_save_every"] and (
-                batch_id % P_cfg["n_save_every"] == 0
-            ):
+            if P_cfg["n_save_every"] and (batch_id % P_cfg["n_save_every"] == 0):
                 _save_model(epoch, is_temp=True)
         _save_model(epoch)
         tracker.epoch_loss = 0.0

spacy/cli/train.py

@@ -93,8 +93,8 @@ def train(
     raw_text, tag_map, morph_rules, weights_data = load_from_paths(config)
     T_cfg = config["training"]
     optimizer = T_cfg["optimizer"]
-    train_corpus = dot_to_object(config, config["training"]["train_corpus"])
-    dev_corpus = dot_to_object(config, config["training"]["dev_corpus"])
+    train_corpus = dot_to_object(config, T_cfg["train_corpus"])
+    dev_corpus = dot_to_object(config, T_cfg["dev_corpus"])
     batcher = T_cfg["batcher"]
     train_logger = T_cfg["logger"]
     # Components that shouldn't be updated during training

spacy/schemas.py

@@ -104,7 +104,7 @@ class TokenPatternOperator(str, Enum):
 StringValue = Union[TokenPatternString, StrictStr]
 NumberValue = Union[TokenPatternNumber, StrictInt, StrictFloat]
 UnderscoreValue = Union[
-    TokenPatternString, TokenPatternNumber, str, int, float, list, bool,
+    TokenPatternString, TokenPatternNumber, str, int, float, list, bool
 ]
 
 

spacy/tests/training/test_readers.py

@@ -26,12 +26,15 @@ def test_readers():
     [components.textcat]
     factory = "textcat"
     """
+
     @registry.readers.register("myreader.v1")
     def myreader() -> Dict[str, Callable[[Language, str], Iterable[Example]]]:
         annots = {"cats": {"POS": 1.0, "NEG": 0.0}}
+
         def reader(nlp: Language):
             doc = nlp.make_doc(f"This is an example")
             return [Example.from_dict(doc, annots)]
+
         return {"train": reader, "dev": reader, "extra": reader, "something": reader}
 
     config = Config().from_str(config_string)