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bf0cdae8d4
* Add long_token_splitter component Add a `long_token_splitter` component for use with transformer pipelines. This component splits up long tokens like URLs into smaller tokens. This is particularly relevant for pretrained pipelines with `strided_spans`, since the user can't change the length of the span `window` and may not wish to preprocess the input texts. The `long_token_splitter` splits tokens that are at least `long_token_length` tokens long into smaller tokens of `split_length` size. Notes: * Since this is intended for use as the first component in a pipeline, the token splitter does not try to preserve any token annotation. * API docs to come when the API is stable. * Adjust API, add test * Fix name in factory
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import pytest
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from spacy.pipeline.functions import merge_subtokens
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from spacy.language import Language
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from spacy.tokens import Span, Doc
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@pytest.fixture
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def doc(en_vocab):
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# fmt: off
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words = ["This", "is", "a", "sentence", ".", "This", "is", "another", "sentence", ".", "And", "a", "third", "."]
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heads = [1, 1, 3, 1, 1, 6, 6, 8, 6, 6, 11, 12, 13, 13]
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deps = ["nsubj", "ROOT", "subtok", "attr", "punct", "nsubj", "ROOT",
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"subtok", "attr", "punct", "subtok", "subtok", "subtok", "ROOT"]
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# fmt: on
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return Doc(en_vocab, words=words, heads=heads, deps=deps)
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@pytest.fixture
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def doc2(en_vocab):
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words = ["I", "like", "New", "York", "in", "Autumn", "."]
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heads = [1, 1, 3, 1, 1, 4, 1]
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tags = ["PRP", "IN", "NNP", "NNP", "IN", "NNP", "."]
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pos = ["PRON", "VERB", "PROPN", "PROPN", "ADP", "PROPN", "PUNCT"]
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deps = ["ROOT", "prep", "compound", "pobj", "prep", "pobj", "punct"]
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doc = Doc(en_vocab, words=words, heads=heads, tags=tags, pos=pos, deps=deps)
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doc.ents = [Span(doc, 2, 4, label="GPE")]
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return doc
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def test_merge_subtokens(doc):
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doc = merge_subtokens(doc)
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# Doc doesn't have spaces, so the result is "And a third ."
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# fmt: off
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assert [t.text for t in doc] == ["This", "is", "a sentence", ".", "This", "is", "another sentence", ".", "And a third ."]
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# fmt: on
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def test_factories_merge_noun_chunks(doc2):
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assert len(doc2) == 7
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nlp = Language()
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merge_noun_chunks = nlp.create_pipe("merge_noun_chunks")
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merge_noun_chunks(doc2)
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assert len(doc2) == 6
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assert doc2[2].text == "New York"
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def test_factories_merge_ents(doc2):
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assert len(doc2) == 7
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assert len(list(doc2.ents)) == 1
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nlp = Language()
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merge_entities = nlp.create_pipe("merge_entities")
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merge_entities(doc2)
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assert len(doc2) == 6
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assert len(list(doc2.ents)) == 1
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assert doc2[2].text == "New York"
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def test_token_splitter():
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nlp = Language()
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config = {"min_length": 20, "split_length": 5}
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token_splitter = nlp.add_pipe("token_splitter", config=config)
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doc = nlp("aaaaabbbbbcccccdddd e f g")
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assert [t.text for t in doc] == ["aaaaabbbbbcccccdddd", "e", "f", "g"]
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doc = nlp("aaaaabbbbbcccccdddddeeeeeff g h i")
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assert [t.text for t in doc] == [
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"aaaaa",
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"bbbbb",
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"ccccc",
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"ddddd",
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"eeeee",
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"ff",
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"g",
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"h",
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"i",
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]
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assert all(len(t.text) <= token_splitter.split_length for t in doc)
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