93c59ebb962058841c6913c8ba4b42830ecf82de,examples/textcat_from_config.py,,main,#,50

Before Change

    // Set the remaining config parameters based on the loaded dataset
    config["model"]["*"]["with_array"]["layer"]["nV"] = len(vocab)
    config["model"]["*"]["softmax"]["nO"] = nr_class

    // Load the config
    loaded_config = thinc.registry.make_from_config(config)

After Change

    // Here we have the model and optimizer, built for us by the registry.
    model = loaded_config["model"]
    model.get_ref("embed").set_dim("nV", len(vocab))
    model.initialize(X=train_X, Y=train_y)

    optimizer = loaded_config["optimizer"]

    // Get training parameters from config

Instances