def METHOD_NAME():
    # Test that the tagger provides a nice error when there's no tagging data / labels
    TEXTCAT_DATA = [
        ("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
        ("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
    ]
    nlp = English()
    nlp.add_pipe("tagger")
    nlp.add_pipe("textcat")
    train_examples = []
    for t in TEXTCAT_DATA:
        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
    with pytest.raises(ValueError):
        nlp.initialize(get_examples=lambda: train_examples)