  
  class ConfigSchemaTraining(BaseModel):
      # fmt: off
      vectors: Optional[StrictStr] = Field(..., title="Path to vectors")
      corpus: Dict[str, Reader] = Field(..., title="Reader for the training and dev data")
      batcher: Batcher = Field(..., title="Batcher for the training data")
      dropout: StrictFloat = Field(..., title="Dropout rate")
      patience: StrictInt = Field(..., title="How many steps to continue without improvement in evaluation score")
      max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
      max_steps: StrictInt = Field(..., title="Maximum number of update steps to train for")
 
</s> remove     corpus: Reader = Field(..., title="Reader for the training data")
 </s> add     corpus: StrictStr = Field(..., title="Path in the config to the training data") </s> remove [training.corpus.dev]
 </s> add [corpora.dev] </s> remove     corpus = P_cfg["corpus"]
 </s> add     corpus = dot_to_object(config, config["pretraining"]["corpus"]) </s> remove [training]

[training.corpus]
 </s> add [corpora]
    dev_corpus: StrictStr = Field(..., title="Path in the config to the dev data")
    train_corpus: StrictStr = Field(..., title="Path in the config to the training data")
  
  class ConfigSchemaTraining(BaseModel):
      # fmt: off
      vectors: Optional[StrictStr] = Field(..., title="Path to vectors")
    dev_corpus: StrictStr = Field(..., title="Path in the config to the dev data")
    train_corpus: StrictStr = Field(..., title="Path in the config to the training data")
      batcher: Batcher = Field(..., title="Batcher for the training data")
      dropout: StrictFloat = Field(..., title="Dropout rate")
      patience: StrictInt = Field(..., title="How many steps to continue without improvement in evaluation score")
      max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
      max_steps: StrictInt = Field(..., title="Maximum number of update steps to train for")
keep keep keep keep replace keep keep keep keep keep
 generalize corpora, dot notation for dev and train corpus 
spacy/schemas.py