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@ -34,17 +34,19 @@ TRAIN_DATA = [
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]
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# fmt: on
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def spans2ints(doc):
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"""Convert doc.spans to nested list of ints for comparison.
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"""Convert doc.spans to nested list of ints for comparison.
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The ints are token indices.
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This is useful for checking consistency of predictions.
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"""
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out = []
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for key, cluster in doc.spans.items():
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out.append( [(ss.start, ss.end) for ss in cluster] )
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out.append([(ss.start, ss.end) for ss in cluster])
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return out
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@pytest.fixture
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def nlp():
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return English()
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@ -70,6 +72,7 @@ def test_not_initialized(nlp):
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with pytest.raises(ValueError, match="E109"):
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nlp(text)
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@pytest.mark.skipif(not has_torch, reason="Torch not available")
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def test_initialized(nlp):
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nlp.add_pipe("coref")
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@ -148,6 +151,7 @@ def test_overfitting_IO(nlp):
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assert spans2ints(docs1[0]) == spans2ints(docs2[0])
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assert spans2ints(docs1[0]) == spans2ints(docs3[0])
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@pytest.mark.skipif(not has_torch, reason="Torch not available")
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def test_tokenization_mismatch(nlp):
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train_examples = []
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@ -158,7 +162,7 @@ def test_tokenization_mismatch(nlp):
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for key, cluster in ref.spans.items():
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char_spans[key] = []
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for span in cluster:
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char_spans[key].append( (span[0].idx, span[-1].idx + len(span[-1])) )
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char_spans[key].append((span[0].idx, span[-1].idx + len(span[-1])))
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with ref.retokenize() as retokenizer:
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# merge "many friends"
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retokenizer.merge(ref[5:7])
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@ -203,6 +207,7 @@ def test_tokenization_mismatch(nlp):
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assert spans2ints(docs1[0]) == spans2ints(docs2[0])
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assert spans2ints(docs1[0]) == spans2ints(docs3[0])
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@pytest.mark.skipif(not has_torch, reason="Torch not available")
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def test_crossing_spans():
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starts = [6, 10, 0, 1, 0, 1, 0, 1, 2, 2, 2]
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@ -215,6 +220,7 @@ def test_crossing_spans():
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guess = sorted(guess)
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assert gold == guess
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@pytest.mark.skipif(not has_torch, reason="Torch not available")
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def test_sentence_map(snlp):
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doc = snlp("I like text. This is text.")
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