spaCy/spacy/tests/pipeline/test_functions.py
Adriane Boyd bf0cdae8d4
Add token_splitter component (#6726)
* 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
2021-01-17 19:54:41 +08:00

77 lines
2.4 KiB
Python

import pytest
from spacy.pipeline.functions import merge_subtokens
from spacy.language import Language
from spacy.tokens import Span, Doc
@pytest.fixture
def doc(en_vocab):
# fmt: off
words = ["This", "is", "a", "sentence", ".", "This", "is", "another", "sentence", ".", "And", "a", "third", "."]
heads = [1, 1, 3, 1, 1, 6, 6, 8, 6, 6, 11, 12, 13, 13]
deps = ["nsubj", "ROOT", "subtok", "attr", "punct", "nsubj", "ROOT",
"subtok", "attr", "punct", "subtok", "subtok", "subtok", "ROOT"]
# fmt: on
return Doc(en_vocab, words=words, heads=heads, deps=deps)
@pytest.fixture
def doc2(en_vocab):
words = ["I", "like", "New", "York", "in", "Autumn", "."]
heads = [1, 1, 3, 1, 1, 4, 1]
tags = ["PRP", "IN", "NNP", "NNP", "IN", "NNP", "."]
pos = ["PRON", "VERB", "PROPN", "PROPN", "ADP", "PROPN", "PUNCT"]
deps = ["ROOT", "prep", "compound", "pobj", "prep", "pobj", "punct"]
doc = Doc(en_vocab, words=words, heads=heads, tags=tags, pos=pos, deps=deps)
doc.ents = [Span(doc, 2, 4, label="GPE")]
return doc
def test_merge_subtokens(doc):
doc = merge_subtokens(doc)
# Doc doesn't have spaces, so the result is "And a third ."
# fmt: off
assert [t.text for t in doc] == ["This", "is", "a sentence", ".", "This", "is", "another sentence", ".", "And a third ."]
# fmt: on
def test_factories_merge_noun_chunks(doc2):
assert len(doc2) == 7
nlp = Language()
merge_noun_chunks = nlp.create_pipe("merge_noun_chunks")
merge_noun_chunks(doc2)
assert len(doc2) == 6
assert doc2[2].text == "New York"
def test_factories_merge_ents(doc2):
assert len(doc2) == 7
assert len(list(doc2.ents)) == 1
nlp = Language()
merge_entities = nlp.create_pipe("merge_entities")
merge_entities(doc2)
assert len(doc2) == 6
assert len(list(doc2.ents)) == 1
assert doc2[2].text == "New York"
def test_token_splitter():
nlp = Language()
config = {"min_length": 20, "split_length": 5}
token_splitter = nlp.add_pipe("token_splitter", config=config)
doc = nlp("aaaaabbbbbcccccdddd e f g")
assert [t.text for t in doc] == ["aaaaabbbbbcccccdddd", "e", "f", "g"]
doc = nlp("aaaaabbbbbcccccdddddeeeeeff g h i")
assert [t.text for t in doc] == [
"aaaaa",
"bbbbb",
"ccccc",
"ddddd",
"eeeee",
"ff",
"g",
"h",
"i",
]
assert all(len(t.text) <= token_splitter.split_length for t in doc)