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Use a single variable for tests
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@ -15,6 +15,8 @@ OPS = get_current_ops()
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SPAN_KEY = "labeled_spans"
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SPANCAT_COMPONENTS = ["spancat", "spancat_exclusive"]
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TRAIN_DATA = [
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("Who is Shaka Khan?", {"spans": {SPAN_KEY: [(7, 17, "PERSON")]}}),
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(
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@ -41,7 +43,7 @@ def make_examples(nlp, data=TRAIN_DATA):
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return train_examples
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@pytest.mark.parametrize("name", ["spancat", "spancat_exclusive"])
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@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
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def test_no_label(name):
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nlp = Language()
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nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
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@ -49,7 +51,7 @@ def test_no_label(name):
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nlp.initialize()
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@pytest.mark.parametrize("name", ["spancat", "spancat_exclusive"])
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@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
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def test_no_resize(name):
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nlp = Language()
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spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
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@ -63,7 +65,7 @@ def test_no_resize(name):
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spancat.add_label("Stuff")
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@pytest.mark.parametrize("name", ["spancat", "spancat_exclusive"])
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@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
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def test_implicit_labels(name):
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nlp = Language()
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spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
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@ -73,7 +75,7 @@ def test_implicit_labels(name):
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assert spancat.labels == ("PERSON", "LOC")
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@pytest.mark.parametrize("name", ["spancat", "spancat_exclusive"])
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@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
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def test_explicit_labels(name):
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nlp = Language()
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spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
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@ -375,7 +377,7 @@ def test_overfitting_IO_overlapping():
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assert set([span.label_ for span in spans2]) == {"LOC", "DOUBLE_LOC"}
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@pytest.mark.parametrize("name", ["spancat", "spancat_exclusive"])
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@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
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def test_zero_suggestions(name):
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# Test with a suggester that returns 0 suggestions
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@ -404,7 +406,7 @@ def test_zero_suggestions(name):
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nlp.update(train_examples, sgd=optimizer)
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@pytest.mark.parametrize("name", ["spancat", "spancat_exclusive"])
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@pytest.mark.parametrize("name", SPANCAT_COMPONENTS)
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def test_set_candidates(name):
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nlp = Language()
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spancat = nlp.add_pipe(name, config={"spans_key": SPAN_KEY})
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