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				|  | @ -191,10 +191,7 @@ def test_set_annotations_span_lengths(min_length, max_length, span_count): | ||||||
|     if min_length is None: |     if min_length is None: | ||||||
|         min_length = 1 |         min_length = 1 | ||||||
| 
 | 
 | ||||||
|     assert all( |     assert all(min_length <= len(span) <= max_length for span in doc.spans[SPANS_KEY]) | ||||||
|         min_length <= len(span) <= max_length |  | ||||||
|         for span in doc.spans[SPANS_KEY] |  | ||||||
|     ) |  | ||||||
| 
 | 
 | ||||||
| 
 | 
 | ||||||
| def test_span_finder_suggester(): | def test_span_finder_suggester(): | ||||||
|  | @ -240,7 +237,11 @@ def test_overfitting_IO(): | ||||||
|     doc = nlp(test_text) |     doc = nlp(test_text) | ||||||
|     spans = doc.spans[span_finder.spans_key] |     spans = doc.spans[span_finder.spans_key] | ||||||
|     assert len(spans) == 3 |     assert len(spans) == 3 | ||||||
|     assert set([span.text for span in spans]) == {"London", "Berlin", "London and Berlin"} |     assert set([span.text for span in spans]) == { | ||||||
|  |         "London", | ||||||
|  |         "Berlin", | ||||||
|  |         "London and Berlin", | ||||||
|  |     } | ||||||
| 
 | 
 | ||||||
|     # Also test the results are still the same after IO |     # Also test the results are still the same after IO | ||||||
|     with make_tempdir() as tmp_dir: |     with make_tempdir() as tmp_dir: | ||||||
|  | @ -249,7 +250,11 @@ def test_overfitting_IO(): | ||||||
|         doc2 = nlp2(test_text) |         doc2 = nlp2(test_text) | ||||||
|         spans2 = doc2.spans[span_finder.spans_key] |         spans2 = doc2.spans[span_finder.spans_key] | ||||||
|         assert len(spans2) == 3 |         assert len(spans2) == 3 | ||||||
|         assert set([span.text for span in spans2]) == {"London", "Berlin", "London and Berlin"} |         assert set([span.text for span in spans2]) == { | ||||||
|  |             "London", | ||||||
|  |             "Berlin", | ||||||
|  |             "London and Berlin", | ||||||
|  |         } | ||||||
| 
 | 
 | ||||||
|     # Test scoring |     # Test scoring | ||||||
|     scores = nlp.evaluate(train_examples) |     scores = nlp.evaluate(train_examples) | ||||||
|  |  | ||||||
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