Dont hard-code for 'corpora' name
@@ -77,12 +77,10 @@ def train(nlp: Language, output_path: Optional[Path]=None) -> None:
# Create iterator, which yields out info after each optimization step.
config = nlp.config.interpolate()
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
+ dot_names = [T["train_corpus"], T["dev_corpus"], T["raw_text"]]
+ train_corpus, dev_corpus, raw_text = resolve_dot_names(config, dot_names)
optimizer T["optimizer"]
score_weights = T["score_weights"]
- # TODO: This might not be called corpora
- corpora = registry.resolve(config["corpora"], schema=ConfigSchemaCorpora)
- train_corpus = dot_to_object({"corpora": corpora}, T["train_corpus"])
- dev_corpus = dot_to_object({"corpora": corpora}, T["dev_corpus"])
batcher = T["batcher"]
train_logger = T["logger"]
before_to_disk = create_before_to_disk_callback(T["before_to_disk"])
@@ -101,7 +99,7 @@ def train(nlp: Language, output_path: Optional[Path]=None) -> None:
patience=T["patience"],
max_steps=T["max_steps"],
eval_frequency=T["eval_frequency"],
- raw_text=None,
+ raw_text=raw_text,
exclude=frozen_components,
)
msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
