mirror of
https://github.com/explosion/spaCy.git
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Merge pull request #10309 from svlandeg/copy/develop
Update master with latest from develop
This commit is contained in:
Sofie Van Landeghem
GitHub
commit
228aaa16b7
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@ -45,6 +45,10 @@ _hangul_syllables = r"\uAC00-\uD7AF"
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_hangul_jamo = r"\u1100-\u11FF"
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_hangul = _hangul_syllables + _hangul_jamo
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_hiragana = r"\u3040-\u309F"
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_katakana = r"\u30A0-\u30FFー"
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_kana = _hiragana + _katakana
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# letters with diacritics - Catalan, Czech, Latin, Latvian, Lithuanian, Polish, Slovak, Turkish, Welsh
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_latin_u_extendedA = (
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r"\u0100\u0102\u0104\u0106\u0108\u010A\u010C\u010E\u0110\u0112\u0114\u0116\u0118\u011A\u011C"
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@ -244,6 +248,7 @@ _uncased = (
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+ _tamil
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+ _telugu
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+ _hangul
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+ _kana
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+ _cjk
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)
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@ -47,6 +47,41 @@ _num_words = [
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]
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_ordinal_words = [
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"primero",
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"segundo",
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"tercero",
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"cuarto",
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"quinto",
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"sexto",
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"séptimo",
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"octavo",
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"noveno",
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"décimo",
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"undécimo",
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"duodécimo",
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"decimotercero",
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"decimocuarto",
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"decimoquinto",
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"decimosexto",
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"decimoséptimo",
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"decimoctavo",
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"decimonoveno",
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"vigésimo",
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"trigésimo",
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"cuadragésimo",
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"quincuagésimo",
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"sexagésimo",
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"septuagésimo",
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"octogésima",
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"nonagésima",
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"centésima",
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"milésima",
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"millonésima",
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"billonésima",
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]
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def like_num(text):
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if text.startswith(("+", "-", "±", "~")):
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text = text[1:]
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@ -57,7 +92,11 @@ def like_num(text):
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num, denom = text.split("/")
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if num.isdigit() and denom.isdigit():
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return True
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if text.lower() in _num_words:
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text_lower = text.lower()
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if text_lower in _num_words:
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return True
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# Check ordinal number
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if text_lower in _ordinal_words:
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return True
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return False
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@ -2,6 +2,7 @@ from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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from .stop_words import STOP_WORDS
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from .lex_attrs import LEX_ATTRS
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from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
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from .syntax_iterators import SYNTAX_ITERATORS
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from ...language import Language, BaseDefaults
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@ -11,6 +12,7 @@ class FinnishDefaults(BaseDefaults):
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tokenizer_exceptions = TOKENIZER_EXCEPTIONS
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lex_attr_getters = LEX_ATTRS
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stop_words = STOP_WORDS
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syntax_iterators = SYNTAX_ITERATORS
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class Finnish(Language):
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79
spacy/lang/fi/syntax_iterators.py
Normal file
79
spacy/lang/fi/syntax_iterators.py
Normal file
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@ -0,0 +1,79 @@
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from typing import Iterator, Tuple, Union
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from ...tokens import Doc, Span
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from ...symbols import NOUN, PROPN, PRON
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from ...errors import Errors
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def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
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"""Detect base noun phrases from a dependency parse. Works on both Doc and Span."""
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labels = [
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"appos",
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"nsubj",
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"nsubj:cop",
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"obj",
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"obl",
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"ROOT",
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]
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extend_labels = [
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"amod",
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"compound",
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"compound:nn",
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"flat:name",
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"nmod",
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"nmod:gobj",
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"nmod:gsubj",
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"nmod:poss",
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"nummod",
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]
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def potential_np_head(word):
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return word.pos in (NOUN, PROPN) and (
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word.dep in np_deps or word.head.pos == PRON
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)
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doc = doclike.doc # Ensure works on both Doc and Span.
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if not doc.has_annotation("DEP"):
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raise ValueError(Errors.E029)
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np_deps = [doc.vocab.strings[label] for label in labels]
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extend_deps = [doc.vocab.strings[label] for label in extend_labels]
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np_label = doc.vocab.strings.add("NP")
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conj_label = doc.vocab.strings.add("conj")
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rbracket = 0
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prev_end = -1
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for i, word in enumerate(doclike):
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if i < rbracket:
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continue
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# Is this a potential independent NP head or coordinated with
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# a NOUN that is itself an independent NP head?
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#
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# e.g. "Terveyden ja hyvinvoinnin laitos"
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if potential_np_head(word) or (
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word.dep == conj_label and potential_np_head(word.head)
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):
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# Try to extend to the left to include adjective/num
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# modifiers, compound words etc.
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lbracket = word.i
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for ldep in word.lefts:
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if ldep.dep in extend_deps:
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lbracket = ldep.left_edge.i
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break
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# Prevent nested chunks from being produced
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if lbracket <= prev_end:
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continue
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rbracket = word.i
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# Try to extend the span to the right to capture
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# appositions and noun modifiers
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for rdep in word.rights:
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if rdep.dep in extend_deps:
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rbracket = rdep.i
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prev_end = rbracket
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yield lbracket, rbracket + 1, np_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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@ -6,16 +6,35 @@ from ...tokens import Doc, Span
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def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
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"""Detect base noun phrases from a dependency parse. Works on Doc and Span."""
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# fmt: off
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labels = ["nsubj", "nsubj:pass", "obj", "iobj", "ROOT", "appos", "nmod", "nmod:poss"]
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# fmt: on
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"""
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Detect base noun phrases from a dependency parse. Works on both Doc and Span.
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"""
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labels = [
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"nsubj",
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"nsubj:pass",
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"obj",
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"obl",
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"obl:agent",
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"obl:arg",
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"obl:mod",
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"nmod",
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"pcomp",
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"appos",
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"ROOT",
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]
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post_modifiers = ["flat", "flat:name", "flat:foreign", "fixed", "compound"]
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doc = doclike.doc # Ensure works on both Doc and Span.
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if not doc.has_annotation("DEP"):
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raise ValueError(Errors.E029)
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np_deps = [doc.vocab.strings[label] for label in labels]
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conj = doc.vocab.strings.add("conj")
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np_deps = {doc.vocab.strings.add(label) for label in labels}
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np_modifs = {doc.vocab.strings.add(modifier) for modifier in post_modifiers}
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np_label = doc.vocab.strings.add("NP")
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adj_label = doc.vocab.strings.add("amod")
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det_label = doc.vocab.strings.add("det")
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det_pos = doc.vocab.strings.add("DET")
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adp_pos = doc.vocab.strings.add("ADP")
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conj_label = doc.vocab.strings.add("conj")
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conj_pos = doc.vocab.strings.add("CCONJ")
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prev_end = -1
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for i, word in enumerate(doclike):
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if word.pos not in (NOUN, PROPN, PRON):
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@ -24,16 +43,45 @@ def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
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if word.left_edge.i <= prev_end:
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continue
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if word.dep in np_deps:
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prev_end = word.right_edge.i
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yield word.left_edge.i, word.right_edge.i + 1, np_label
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elif word.dep == conj:
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right_childs = list(word.rights)
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right_child = right_childs[0] if right_childs else None
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if right_child:
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if (
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right_child.dep == adj_label
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): # allow chain of adjectives by expanding to right
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right_end = right_child.right_edge
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elif (
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right_child.dep == det_label and right_child.pos == det_pos
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): # cut relative pronouns here
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right_end = right_child
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elif right_child.dep in np_modifs: # Check if we can expand to right
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right_end = word.right_edge
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else:
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right_end = word
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else:
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right_end = word
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prev_end = right_end.i
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left_index = word.left_edge.i
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left_index = (
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left_index + 1 if word.left_edge.pos == adp_pos else left_index
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)
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yield left_index, right_end.i + 1, np_label
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elif word.dep == conj_label:
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head = word.head
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while head.dep == conj and head.head.i < head.i:
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while head.dep == conj_label and head.head.i < head.i:
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head = head.head
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# If the head is an NP, and we're coordinated to it, we're an NP
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if head.dep in np_deps:
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prev_end = word.right_edge.i
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yield word.left_edge.i, word.right_edge.i + 1, np_label
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prev_end = word.i
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left_index = word.left_edge.i # eliminate left attached conjunction
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left_index = (
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left_index + 1 if word.left_edge.pos == conj_pos else left_index
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)
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yield left_index, word.i + 1, np_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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@ -6,13 +6,15 @@ from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_INFIXES
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from ...language import Language, BaseDefaults
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from .lemmatizer import ItalianLemmatizer
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from .syntax_iterators import SYNTAX_ITERATORS
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class ItalianDefaults(BaseDefaults):
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tokenizer_exceptions = TOKENIZER_EXCEPTIONS
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stop_words = STOP_WORDS
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prefixes = TOKENIZER_PREFIXES
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infixes = TOKENIZER_INFIXES
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stop_words = STOP_WORDS
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syntax_iterators = SYNTAX_ITERATORS
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class Italian(Language):
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@ -10,18 +10,18 @@ avresti avrete avrà avrò avuta avute avuti avuto
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basta bene benissimo brava bravo
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casa caso cento certa certe certi certo che chi chicchessia chiunque ci
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casa caso cento certa certe certi certo che chi chicchessia chiunque ci c'
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ciascuna ciascuno cima cio cioe circa citta città co codesta codesti codesto
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cogli coi col colei coll coloro colui come cominci comunque con concernente
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conciliarsi conclusione consiglio contro cortesia cos cosa cosi così cui
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da dagl dagli dai dal dall dalla dalle dallo dappertutto davanti degl degli
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dei del dell della delle dello dentro detto deve di dice dietro dire
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d' da dagl dagli dai dal dall dall' dalla dalle dallo dappertutto davanti degl degli
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dei del dell dell' della delle dello dentro detto deve di dice dietro dire
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dirimpetto diventa diventare diventato dopo dov dove dovra dovrà dovunque due
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dunque durante
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ebbe ebbero ebbi ecc ecco ed effettivamente egli ella entrambi eppure era
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erano eravamo eravate eri ero esempio esse essendo esser essere essi ex
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e ebbe ebbero ebbi ecc ecco ed effettivamente egli ella entrambi eppure era
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erano eravamo eravate eri ero esempio esse essendo esser essere essi ex è
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fa faccia facciamo facciano facciate faccio facemmo facendo facesse facessero
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facessi facessimo faceste facesti faceva facevamo facevano facevate facevi
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@ -30,21 +30,21 @@ fareste faresti farete farà farò fatto favore fece fecero feci fin finalmente
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finche fine fino forse forza fosse fossero fossi fossimo foste fosti fra
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frattempo fu fui fummo fuori furono futuro generale
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gia già giacche giorni giorno gli gliela gliele glieli glielo gliene governo
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gia già giacche giorni giorno gli gl' gliela gliele glieli glielo gliene governo
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grande grazie gruppo
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ha haha hai hanno ho
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ieri il improvviso in inc infatti inoltre insieme intanto intorno invece io
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la là lasciato lato lavoro le lei li lo lontano loro lui lungo luogo
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l' la là lasciato lato lavoro le lei li lo lontano loro lui lungo luogo
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ma macche magari maggior mai male malgrado malissimo mancanza marche me
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m' ma macche magari maggior mai male malgrado malissimo mancanza marche me
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medesimo mediante meglio meno mentre mesi mezzo mi mia mie miei mila miliardi
|
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milioni minimi ministro mio modo molti moltissimo molto momento mondo mosto
|
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nazionale ne negl negli nei nel nell nella nelle nello nemmeno neppure nessun
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nessuna nessuno niente no noi non nondimeno nonostante nonsia nostra nostre
|
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nazionale ne negl negli nei nel nell nella nelle nello nemmeno neppure nessun nessun'
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nessuna nessuno nient' niente no noi non nondimeno nonostante nonsia nostra nostre
|
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nostri nostro novanta nove nulla nuovo
|
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od oggi ogni ognuna ognuno oltre oppure ora ore osi ossia ottanta otto
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|
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@ -56,12 +56,12 @@ potrebbe preferibilmente presa press prima primo principalmente probabilmente
|
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proprio puo può pure purtroppo
|
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|
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qualche qualcosa qualcuna qualcuno quale quali qualunque quando quanta quante
|
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quanti quanto quantunque quasi quattro quel quella quelle quelli quello quest
|
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quanti quanto quantunque quasi quattro quel quel' quella quelle quelli quello quest quest'
|
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questa queste questi questo qui quindi
|
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|
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realmente recente recentemente registrazione relativo riecco salvo
|
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|
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sara sarà sarai saranno sarebbe sarebbero sarei saremmo saremo sareste
|
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s' sara sarà sarai saranno sarebbe sarebbero sarei saremmo saremo sareste
|
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saresti sarete saro sarò scola scopo scorso se secondo seguente seguito sei
|
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sembra sembrare sembrato sembri sempre senza sette si sia siamo siano siate
|
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siete sig solito solo soltanto sono sopra sotto spesso srl sta stai stando
|
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|
|
@ -72,12 +72,12 @@ steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua
|
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subito successivamente successivo sue sugl sugli sui sul sull sulla sulle
|
||||
sullo suo suoi
|
||||
|
||||
tale tali talvolta tanto te tempo ti titolo tra tranne tre trenta
|
||||
t' tale tali talvolta tanto te tempo ti titolo tra tranne tre trenta
|
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troppo trovato tu tua tue tuo tuoi tutta tuttavia tutte tutti tutto
|
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|
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uguali ulteriore ultimo un una uno uomo
|
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uguali ulteriore ultimo un un' una uno uomo
|
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|
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va vale vari varia varie vario verso vi via vicino visto vita voi volta volte
|
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v' va vale vari varia varie vario verso vi via vicino visto vita voi volta volte
|
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vostra vostre vostri vostro
|
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""".split()
|
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)
|
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|
|
|
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86
spacy/lang/it/syntax_iterators.py
Normal file
86
spacy/lang/it/syntax_iterators.py
Normal file
|
|
@ -0,0 +1,86 @@
|
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from typing import Union, Iterator, Tuple
|
||||
|
||||
from ...symbols import NOUN, PROPN, PRON
|
||||
from ...errors import Errors
|
||||
from ...tokens import Doc, Span
|
||||
|
||||
|
||||
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
|
||||
"""
|
||||
Detect base noun phrases from a dependency parse. Works on both Doc and Span.
|
||||
"""
|
||||
labels = [
|
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"nsubj",
|
||||
"nsubj:pass",
|
||||
"obj",
|
||||
"obl",
|
||||
"obl:agent",
|
||||
"nmod",
|
||||
"pcomp",
|
||||
"appos",
|
||||
"ROOT",
|
||||
]
|
||||
post_modifiers = ["flat", "flat:name", "fixed", "compound"]
|
||||
dets = ["det", "det:poss"]
|
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doc = doclike.doc # Ensure works on both Doc and Span.
|
||||
if not doc.has_annotation("DEP"):
|
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raise ValueError(Errors.E029)
|
||||
np_deps = {doc.vocab.strings.add(label) for label in labels}
|
||||
np_modifs = {doc.vocab.strings.add(modifier) for modifier in post_modifiers}
|
||||
np_label = doc.vocab.strings.add("NP")
|
||||
adj_label = doc.vocab.strings.add("amod")
|
||||
det_labels = {doc.vocab.strings.add(det) for det in dets}
|
||||
det_pos = doc.vocab.strings.add("DET")
|
||||
adp_label = doc.vocab.strings.add("ADP")
|
||||
conj = doc.vocab.strings.add("conj")
|
||||
conj_pos = doc.vocab.strings.add("CCONJ")
|
||||
prev_end = -1
|
||||
for i, word in enumerate(doclike):
|
||||
if word.pos not in (NOUN, PROPN, PRON):
|
||||
continue
|
||||
# Prevent nested chunks from being produced
|
||||
if word.left_edge.i <= prev_end:
|
||||
continue
|
||||
if word.dep in np_deps:
|
||||
right_childs = list(word.rights)
|
||||
right_child = right_childs[0] if right_childs else None
|
||||
|
||||
if right_child:
|
||||
if (
|
||||
right_child.dep == adj_label
|
||||
): # allow chain of adjectives by expanding to right
|
||||
right_end = right_child.right_edge
|
||||
elif (
|
||||
right_child.dep in det_labels and right_child.pos == det_pos
|
||||
): # cut relative pronouns here
|
||||
right_end = right_child
|
||||
elif right_child.dep in np_modifs: # Check if we can expand to right
|
||||
right_end = word.right_edge
|
||||
else:
|
||||
right_end = word
|
||||
else:
|
||||
right_end = word
|
||||
prev_end = right_end.i
|
||||
|
||||
left_index = word.left_edge.i
|
||||
left_index = (
|
||||
left_index + 1 if word.left_edge.pos == adp_label else left_index
|
||||
)
|
||||
|
||||
yield left_index, right_end.i + 1, np_label
|
||||
elif word.dep == conj:
|
||||
head = word.head
|
||||
while head.dep == conj and head.head.i < head.i:
|
||||
head = head.head
|
||||
# If the head is an NP, and we're coordinated to it, we're an NP
|
||||
if head.dep in np_deps:
|
||||
prev_end = word.i
|
||||
|
||||
left_index = word.left_edge.i # eliminate left attached conjunction
|
||||
left_index = (
|
||||
left_index + 1 if word.left_edge.pos == conj_pos else left_index
|
||||
)
|
||||
yield left_index, word.i + 1, np_label
|
||||
|
||||
|
||||
SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
|
||||
|
|
@ -4,46 +4,42 @@ alle allerede alt and andre annen annet at av
|
|||
|
||||
bak bare bedre beste blant ble bli blir blitt bris by både
|
||||
|
||||
da dag de del dem den denne der dermed det dette disse drept du
|
||||
da dag de del dem den denne der dermed det dette disse du
|
||||
|
||||
eller en enn er et ett etter
|
||||
|
||||
fem fikk fire fjor flere folk for fortsatt fotball fra fram frankrike fredag
|
||||
fem fikk fire fjor flere folk for fortsatt fra fram
|
||||
funnet få får fått før først første
|
||||
|
||||
gang gi gikk gjennom gjorde gjort gjør gjøre god godt grunn gå går
|
||||
|
||||
ha hadde ham han hans har hele helt henne hennes her hun hva hvor hvordan
|
||||
hvorfor
|
||||
ha hadde ham han hans har hele helt henne hennes her hun
|
||||
|
||||
i ifølge igjen ikke ingen inn
|
||||
|
||||
ja jeg
|
||||
|
||||
kamp kampen kan kl klart kom komme kommer kontakt kort kroner kunne kveld
|
||||
kvinner
|
||||
|
||||
la laget land landet langt leder ligger like litt løpet lørdag
|
||||
la laget land landet langt leder ligger like litt løpet
|
||||
|
||||
man mandag mange mannen mars med meg mellom men mener menn mennesker mens mer
|
||||
millioner minutter mot msci mye må mål måtte
|
||||
man mange med meg mellom men mener mennesker mens mer mot mye må mål måtte
|
||||
|
||||
ned neste noe noen nok norge norsk norske ntb ny nye nå når
|
||||
ned neste noe noen nok ny nye nå når
|
||||
|
||||
og også om onsdag opp opplyser oslo oss over
|
||||
og også om opp opplyser oss over
|
||||
|
||||
personer plass poeng politidistrikt politiet president prosent på
|
||||
personer plass poeng på
|
||||
|
||||
regjeringen runde rundt russland
|
||||
runde rundt
|
||||
|
||||
sa saken samme sammen samtidig satt se seg seks selv senere september ser sett
|
||||
sa saken samme sammen samtidig satt se seg seks selv senere ser sett
|
||||
siden sier sin sine siste sitt skal skriver skulle slik som sted stedet stor
|
||||
store står sverige svært så søndag
|
||||
store står svært så
|
||||
|
||||
ta tatt tid tidligere til tilbake tillegg tirsdag to tok torsdag tre tror
|
||||
tyskland
|
||||
ta tatt tid tidligere til tilbake tillegg tok tror
|
||||
|
||||
under usa ut uten utenfor
|
||||
under ut uten utenfor
|
||||
|
||||
vant var ved veldig vi videre viktig vil ville viser vår være vært
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +1,10 @@
|
|||
# Source: https://github.com/stopwords-iso/stopwords-sl
|
||||
# TODO: probably needs to be tidied up – the list seems to have month names in
|
||||
# it, which shouldn't be considered stop words.
|
||||
# Removed various words that are not normally considered stop words, such as months.
|
||||
|
||||
STOP_WORDS = set(
|
||||
"""
|
||||
a
|
||||
ali
|
||||
april
|
||||
avgust
|
||||
b
|
||||
bi
|
||||
bil
|
||||
|
|
@ -19,7 +16,6 @@ biti
|
|||
blizu
|
||||
bo
|
||||
bodo
|
||||
bojo
|
||||
bolj
|
||||
bom
|
||||
bomo
|
||||
|
|
@ -37,16 +33,6 @@ da
|
|||
daleč
|
||||
dan
|
||||
danes
|
||||
datum
|
||||
december
|
||||
deset
|
||||
deseta
|
||||
deseti
|
||||
deseto
|
||||
devet
|
||||
deveta
|
||||
deveti
|
||||
deveto
|
||||
do
|
||||
dober
|
||||
dobra
|
||||
|
|
@ -54,16 +40,7 @@ dobri
|
|||
dobro
|
||||
dokler
|
||||
dol
|
||||
dolg
|
||||
dolga
|
||||
dolgi
|
||||
dovolj
|
||||
drug
|
||||
druga
|
||||
drugi
|
||||
drugo
|
||||
dva
|
||||
dve
|
||||
e
|
||||
eden
|
||||
en
|
||||
|
|
@ -74,7 +51,6 @@ enkrat
|
|||
eno
|
||||
etc.
|
||||
f
|
||||
februar
|
||||
g
|
||||
g.
|
||||
ga
|
||||
|
|
@ -93,16 +69,12 @@ iv
|
|||
ix
|
||||
iz
|
||||
j
|
||||
januar
|
||||
jaz
|
||||
je
|
||||
ji
|
||||
jih
|
||||
jim
|
||||
jo
|
||||
julij
|
||||
junij
|
||||
jutri
|
||||
k
|
||||
kadarkoli
|
||||
kaj
|
||||
|
|
@ -123,41 +95,23 @@ kje
|
|||
kjer
|
||||
kjerkoli
|
||||
ko
|
||||
koder
|
||||
koderkoli
|
||||
koga
|
||||
komu
|
||||
kot
|
||||
kratek
|
||||
kratka
|
||||
kratke
|
||||
kratki
|
||||
l
|
||||
lahka
|
||||
lahke
|
||||
lahki
|
||||
lahko
|
||||
le
|
||||
lep
|
||||
lepa
|
||||
lepe
|
||||
lepi
|
||||
lepo
|
||||
leto
|
||||
m
|
||||
maj
|
||||
majhen
|
||||
majhna
|
||||
majhni
|
||||
malce
|
||||
malo
|
||||
manj
|
||||
marec
|
||||
me
|
||||
med
|
||||
medtem
|
||||
mene
|
||||
mesec
|
||||
mi
|
||||
midva
|
||||
midve
|
||||
|
|
@ -183,7 +137,6 @@ najmanj
|
|||
naju
|
||||
največ
|
||||
nam
|
||||
narobe
|
||||
nas
|
||||
nato
|
||||
nazaj
|
||||
|
|
@ -192,7 +145,6 @@ naša
|
|||
naše
|
||||
ne
|
||||
nedavno
|
||||
nedelja
|
||||
nek
|
||||
neka
|
||||
nekaj
|
||||
|
|
@ -236,7 +188,6 @@ njuna
|
|||
njuno
|
||||
no
|
||||
nocoj
|
||||
november
|
||||
npr.
|
||||
o
|
||||
ob
|
||||
|
|
@ -244,51 +195,23 @@ oba
|
|||
obe
|
||||
oboje
|
||||
od
|
||||
odprt
|
||||
odprta
|
||||
odprti
|
||||
okoli
|
||||
oktober
|
||||
on
|
||||
onadva
|
||||
one
|
||||
oni
|
||||
onidve
|
||||
osem
|
||||
osma
|
||||
osmi
|
||||
osmo
|
||||
oz.
|
||||
p
|
||||
pa
|
||||
pet
|
||||
peta
|
||||
petek
|
||||
peti
|
||||
peto
|
||||
po
|
||||
pod
|
||||
pogosto
|
||||
poleg
|
||||
poln
|
||||
polna
|
||||
polni
|
||||
polno
|
||||
ponavadi
|
||||
ponedeljek
|
||||
ponovno
|
||||
potem
|
||||
povsod
|
||||
pozdravljen
|
||||
pozdravljeni
|
||||
prav
|
||||
prava
|
||||
prave
|
||||
pravi
|
||||
pravo
|
||||
prazen
|
||||
prazna
|
||||
prazno
|
||||
prbl.
|
||||
precej
|
||||
pred
|
||||
|
|
@ -297,19 +220,10 @@ preko
|
|||
pri
|
||||
pribl.
|
||||
približno
|
||||
primer
|
||||
pripravljen
|
||||
pripravljena
|
||||
pripravljeni
|
||||
proti
|
||||
prva
|
||||
prvi
|
||||
prvo
|
||||
r
|
||||
ravno
|
||||
redko
|
||||
res
|
||||
reč
|
||||
s
|
||||
saj
|
||||
sam
|
||||
|
|
@ -321,29 +235,17 @@ se
|
|||
sebe
|
||||
sebi
|
||||
sedaj
|
||||
sedem
|
||||
sedma
|
||||
sedmi
|
||||
sedmo
|
||||
sem
|
||||
september
|
||||
seveda
|
||||
si
|
||||
sicer
|
||||
skoraj
|
||||
skozi
|
||||
slab
|
||||
smo
|
||||
so
|
||||
sobota
|
||||
spet
|
||||
sreda
|
||||
srednja
|
||||
srednji
|
||||
sta
|
||||
ste
|
||||
stran
|
||||
stvar
|
||||
sva
|
||||
t
|
||||
ta
|
||||
|
|
@ -358,10 +260,6 @@ te
|
|||
tebe
|
||||
tebi
|
||||
tega
|
||||
težak
|
||||
težka
|
||||
težki
|
||||
težko
|
||||
ti
|
||||
tista
|
||||
tiste
|
||||
|
|
@ -371,11 +269,6 @@ tj.
|
|||
tja
|
||||
to
|
||||
toda
|
||||
torek
|
||||
tretja
|
||||
tretje
|
||||
tretji
|
||||
tri
|
||||
tu
|
||||
tudi
|
||||
tukaj
|
||||
|
|
@ -392,10 +285,6 @@ vaša
|
|||
vaše
|
||||
ve
|
||||
vedno
|
||||
velik
|
||||
velika
|
||||
veliki
|
||||
veliko
|
||||
vendar
|
||||
ves
|
||||
več
|
||||
|
|
@ -403,10 +292,6 @@ vi
|
|||
vidva
|
||||
vii
|
||||
viii
|
||||
visok
|
||||
visoka
|
||||
visoke
|
||||
visoki
|
||||
vsa
|
||||
vsaj
|
||||
vsak
|
||||
|
|
@ -420,34 +305,21 @@ vsega
|
|||
vsi
|
||||
vso
|
||||
včasih
|
||||
včeraj
|
||||
x
|
||||
z
|
||||
za
|
||||
zadaj
|
||||
zadnji
|
||||
zakaj
|
||||
zaprta
|
||||
zaprti
|
||||
zaprto
|
||||
zdaj
|
||||
zelo
|
||||
zunaj
|
||||
č
|
||||
če
|
||||
često
|
||||
četrta
|
||||
četrtek
|
||||
četrti
|
||||
četrto
|
||||
čez
|
||||
čigav
|
||||
š
|
||||
šest
|
||||
šesta
|
||||
šesti
|
||||
šesto
|
||||
štiri
|
||||
ž
|
||||
že
|
||||
""".split()
|
||||
|
|
|
|||
|
|
@ -6,19 +6,30 @@ from ...util import update_exc
|
|||
_exc = {}
|
||||
|
||||
for exc_data in [
|
||||
{ORTH: "обл.", NORM: "область"},
|
||||
{ORTH: "р-н.", NORM: "район"},
|
||||
{ORTH: "р-н", NORM: "район"},
|
||||
{ORTH: "м.", NORM: "місто"},
|
||||
{ORTH: "вул.", NORM: "вулиця"},
|
||||
{ORTH: "ім.", NORM: "імені"},
|
||||
{ORTH: "просп.", NORM: "проспект"},
|
||||
{ORTH: "пр-кт", NORM: "проспект"},
|
||||
{ORTH: "бул.", NORM: "бульвар"},
|
||||
{ORTH: "пров.", NORM: "провулок"},
|
||||
{ORTH: "пл.", NORM: "площа"},
|
||||
{ORTH: "майд.", NORM: "майдан"},
|
||||
{ORTH: "мкр.", NORM: "мікрорайон"},
|
||||
{ORTH: "ст.", NORM: "станція"},
|
||||
{ORTH: "ж/м", NORM: "житловий масив"},
|
||||
{ORTH: "наб.", NORM: "набережна"},
|
||||
{ORTH: "в/ч", NORM: "військова частина"},
|
||||
{ORTH: "в/м", NORM: "військове містечко"},
|
||||
{ORTH: "оз.", NORM: "озеро"},
|
||||
{ORTH: "ім.", NORM: "імені"},
|
||||
{ORTH: "г.", NORM: "гора"},
|
||||
{ORTH: "п.", NORM: "пан"},
|
||||
{ORTH: "м.", NORM: "місто"},
|
||||
{ORTH: "проф.", NORM: "професор"},
|
||||
{ORTH: "акад.", NORM: "академік"},
|
||||
{ORTH: "доц.", NORM: "доцент"},
|
||||
{ORTH: "оз.", NORM: "озеро"},
|
||||
]:
|
||||
_exc[exc_data[ORTH]] = [exc_data]
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ from libc.string cimport memcpy, memset
|
|||
from libc.stdlib cimport calloc, free
|
||||
from libc.stdint cimport uint32_t, uint64_t
|
||||
cimport libcpp
|
||||
from libcpp.unordered_map cimport unordered_map
|
||||
from libcpp.vector cimport vector
|
||||
from libcpp.set cimport set
|
||||
from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
|
||||
|
|
@ -30,8 +31,8 @@ cdef cppclass StateC:
|
|||
vector[int] _stack
|
||||
vector[int] _rebuffer
|
||||
vector[SpanC] _ents
|
||||
vector[ArcC] _left_arcs
|
||||
vector[ArcC] _right_arcs
|
||||
unordered_map[int, vector[ArcC]] _left_arcs
|
||||
unordered_map[int, vector[ArcC]] _right_arcs
|
||||
vector[libcpp.bool] _unshiftable
|
||||
set[int] _sent_starts
|
||||
TokenC _empty_token
|
||||
|
|
@ -160,15 +161,22 @@ cdef cppclass StateC:
|
|||
else:
|
||||
return &this._sent[i]
|
||||
|
||||
void get_arcs(vector[ArcC]* arcs) nogil const:
|
||||
for i in range(this._left_arcs.size()):
|
||||
arc = this._left_arcs.at(i)
|
||||
if arc.head != -1 and arc.child != -1:
|
||||
arcs.push_back(arc)
|
||||
for i in range(this._right_arcs.size()):
|
||||
arc = this._right_arcs.at(i)
|
||||
if arc.head != -1 and arc.child != -1:
|
||||
arcs.push_back(arc)
|
||||
void map_get_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, vector[ArcC]* out) nogil const:
|
||||
cdef const vector[ArcC]* arcs
|
||||
head_arcs_it = heads_arcs.const_begin()
|
||||
while head_arcs_it != heads_arcs.const_end():
|
||||
arcs = &deref(head_arcs_it).second
|
||||
arcs_it = arcs.const_begin()
|
||||
while arcs_it != arcs.const_end():
|
||||
arc = deref(arcs_it)
|
||||
if arc.head != -1 and arc.child != -1:
|
||||
out.push_back(arc)
|
||||
incr(arcs_it)
|
||||
incr(head_arcs_it)
|
||||
|
||||
void get_arcs(vector[ArcC]* out) nogil const:
|
||||
this.map_get_arcs(this._left_arcs, out)
|
||||
this.map_get_arcs(this._right_arcs, out)
|
||||
|
||||
int H(int child) nogil const:
|
||||
if child >= this.length or child < 0:
|
||||
|
|
@ -182,37 +190,35 @@ cdef cppclass StateC:
|
|||
else:
|
||||
return this._ents.back().start
|
||||
|
||||
int L(int head, int idx) nogil const:
|
||||
if idx < 1 or this._left_arcs.size() == 0:
|
||||
int nth_child(const unordered_map[int, vector[ArcC]]& heads_arcs, int head, int idx) nogil const:
|
||||
if idx < 1:
|
||||
return -1
|
||||
|
||||
# Work backwards through left-arcs to find the arc at the
|
||||
head_arcs_it = heads_arcs.const_find(head)
|
||||
if head_arcs_it == heads_arcs.const_end():
|
||||
return -1
|
||||
|
||||
cdef const vector[ArcC]* arcs = &deref(head_arcs_it).second
|
||||
|
||||
# Work backwards through arcs to find the arc at the
|
||||
# requested index more quickly.
|
||||
cdef size_t child_index = 0
|
||||
it = this._left_arcs.const_rbegin()
|
||||
while it != this._left_arcs.rend():
|
||||
arc = deref(it)
|
||||
if arc.head == head and arc.child != -1 and arc.child < head:
|
||||
arcs_it = arcs.const_rbegin()
|
||||
while arcs_it != arcs.const_rend() and child_index != idx:
|
||||
arc = deref(arcs_it)
|
||||
if arc.child != -1:
|
||||
child_index += 1
|
||||
if child_index == idx:
|
||||
return arc.child
|
||||
incr(it)
|
||||
incr(arcs_it)
|
||||
|
||||
return -1
|
||||
|
||||
int L(int head, int idx) nogil const:
|
||||
return this.nth_child(this._left_arcs, head, idx)
|
||||
|
||||
int R(int head, int idx) nogil const:
|
||||
if idx < 1 or this._right_arcs.size() == 0:
|
||||
return -1
|
||||
cdef vector[int] rights
|
||||
for i in range(this._right_arcs.size()):
|
||||
arc = this._right_arcs.at(i)
|
||||
if arc.head == head and arc.child != -1 and arc.child > head:
|
||||
rights.push_back(arc.child)
|
||||
idx = (<int>rights.size()) - idx
|
||||
if idx < 0:
|
||||
return -1
|
||||
else:
|
||||
return rights.at(idx)
|
||||
return this.nth_child(this._right_arcs, head, idx)
|
||||
|
||||
bint empty() nogil const:
|
||||
return this._stack.size() == 0
|
||||
|
|
@ -253,22 +259,29 @@ cdef cppclass StateC:
|
|||
|
||||
int r_edge(int word) nogil const:
|
||||
return word
|
||||
|
||||
int n_L(int head) nogil const:
|
||||
|
||||
int n_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, int head) nogil const:
|
||||
cdef int n = 0
|
||||
for i in range(this._left_arcs.size()):
|
||||
arc = this._left_arcs.at(i)
|
||||
if arc.head == head and arc.child != -1 and arc.child < arc.head:
|
||||
head_arcs_it = heads_arcs.const_find(head)
|
||||
if head_arcs_it == heads_arcs.const_end():
|
||||
return n
|
||||
|
||||
cdef const vector[ArcC]* arcs = &deref(head_arcs_it).second
|
||||
arcs_it = arcs.const_begin()
|
||||
while arcs_it != arcs.end():
|
||||
arc = deref(arcs_it)
|
||||
if arc.child != -1:
|
||||
n += 1
|
||||
incr(arcs_it)
|
||||
|
||||
return n
|
||||
|
||||
|
||||
int n_L(int head) nogil const:
|
||||
return n_arcs(this._left_arcs, head)
|
||||
|
||||
int n_R(int head) nogil const:
|
||||
cdef int n = 0
|
||||
for i in range(this._right_arcs.size()):
|
||||
arc = this._right_arcs.at(i)
|
||||
if arc.head == head and arc.child != -1 and arc.child > arc.head:
|
||||
n += 1
|
||||
return n
|
||||
return n_arcs(this._right_arcs, head)
|
||||
|
||||
bint stack_is_connected() nogil const:
|
||||
return False
|
||||
|
|
@ -328,19 +341,20 @@ cdef cppclass StateC:
|
|||
arc.child = child
|
||||
arc.label = label
|
||||
if head > child:
|
||||
this._left_arcs.push_back(arc)
|
||||
this._left_arcs[arc.head].push_back(arc)
|
||||
else:
|
||||
this._right_arcs.push_back(arc)
|
||||
this._right_arcs[arc.head].push_back(arc)
|
||||
this._heads[child] = head
|
||||
|
||||
void del_arc(int h_i, int c_i) nogil:
|
||||
cdef vector[ArcC]* arcs
|
||||
if h_i > c_i:
|
||||
arcs = &this._left_arcs
|
||||
else:
|
||||
arcs = &this._right_arcs
|
||||
void map_del_arc(unordered_map[int, vector[ArcC]]* heads_arcs, int h_i, int c_i) nogil:
|
||||
arcs_it = heads_arcs.find(h_i)
|
||||
if arcs_it == heads_arcs.end():
|
||||
return
|
||||
|
||||
arcs = &deref(arcs_it).second
|
||||
if arcs.size() == 0:
|
||||
return
|
||||
|
||||
arc = arcs.back()
|
||||
if arc.head == h_i and arc.child == c_i:
|
||||
arcs.pop_back()
|
||||
|
|
@ -353,6 +367,12 @@ cdef cppclass StateC:
|
|||
arc.label = 0
|
||||
break
|
||||
|
||||
void del_arc(int h_i, int c_i) nogil:
|
||||
if h_i > c_i:
|
||||
this.map_del_arc(&this._left_arcs, h_i, c_i)
|
||||
else:
|
||||
this.map_del_arc(&this._right_arcs, h_i, c_i)
|
||||
|
||||
SpanC get_ent() nogil const:
|
||||
cdef SpanC ent
|
||||
if this._ents.size() == 0:
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# cython: infer_types=True, profile=True, binding=True
|
||||
from itertools import islice
|
||||
from typing import Optional, Callable
|
||||
from itertools import islice
|
||||
|
||||
import srsly
|
||||
from thinc.api import Model, SequenceCategoricalCrossentropy, Config
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
import numpy
|
||||
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
|
||||
from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
|
||||
from thinc.api import Optimizer
|
||||
from thinc.types import Ragged, Ints2d, Floats2d, Ints1d
|
||||
|
||||
import numpy
|
||||
|
||||
from ..compat import Protocol, runtime_checkable
|
||||
from ..scorer import Scorer
|
||||
from ..language import Language
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
from itertools import islice
|
||||
from typing import Iterable, Tuple, Optional, Dict, List, Callable, Any
|
||||
from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
|
||||
from thinc.types import Floats2d
|
||||
import numpy
|
||||
from itertools import islice
|
||||
|
||||
from .trainable_pipe import TrainablePipe
|
||||
from ..language import Language
|
||||
|
|
@ -158,6 +158,13 @@ class TextCategorizer(TrainablePipe):
|
|||
self.cfg = dict(cfg)
|
||||
self.scorer = scorer
|
||||
|
||||
@property
|
||||
def support_missing_values(self):
|
||||
# There are no missing values as the textcat should always
|
||||
# predict exactly one label. All other labels are 0.0
|
||||
# Subclasses may override this property to change internal behaviour.
|
||||
return False
|
||||
|
||||
@property
|
||||
def labels(self) -> Tuple[str]:
|
||||
"""RETURNS (Tuple[str]): The labels currently added to the component.
|
||||
|
|
@ -294,7 +301,7 @@ class TextCategorizer(TrainablePipe):
|
|||
for j, label in enumerate(self.labels):
|
||||
if label in eg.reference.cats:
|
||||
truths[i, j] = eg.reference.cats[label]
|
||||
else:
|
||||
elif self.support_missing_values:
|
||||
not_missing[i, j] = 0.0
|
||||
truths = self.model.ops.asarray(truths) # type: ignore
|
||||
return truths, not_missing # type: ignore
|
||||
|
|
@ -313,9 +320,9 @@ class TextCategorizer(TrainablePipe):
|
|||
self._validate_categories(examples)
|
||||
truths, not_missing = self._examples_to_truth(examples)
|
||||
not_missing = self.model.ops.asarray(not_missing) # type: ignore
|
||||
d_scores = (scores - truths) / scores.shape[0]
|
||||
d_scores = (scores - truths)
|
||||
d_scores *= not_missing
|
||||
mean_square_error = (d_scores**2).sum(axis=1).mean()
|
||||
mean_square_error = (d_scores**2).mean()
|
||||
return float(mean_square_error), d_scores
|
||||
|
||||
def add_label(self, label: str) -> int:
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
from itertools import islice
|
||||
from typing import Iterable, Optional, Dict, List, Callable, Any
|
||||
|
||||
from thinc.api import Model, Config
|
||||
from thinc.types import Floats2d
|
||||
from thinc.api import Model, Config
|
||||
|
||||
from itertools import islice
|
||||
|
||||
from ..language import Language
|
||||
from ..training import Example, validate_get_examples
|
||||
|
|
@ -158,6 +158,10 @@ class MultiLabel_TextCategorizer(TextCategorizer):
|
|||
self.cfg = dict(cfg)
|
||||
self.scorer = scorer
|
||||
|
||||
@property
|
||||
def support_missing_values(self):
|
||||
return True
|
||||
|
||||
def initialize( # type: ignore[override]
|
||||
self,
|
||||
get_examples: Callable[[], Iterable[Example]],
|
||||
|
|
|
|||
|
|
@ -445,7 +445,8 @@ class Scorer:
|
|||
getter(doc, attr) should return the values for the individual doc.
|
||||
labels (Iterable[str]): The set of possible labels. Defaults to [].
|
||||
multi_label (bool): Whether the attribute allows multiple labels.
|
||||
Defaults to True.
|
||||
Defaults to True. When set to False (exclusive labels), missing
|
||||
gold labels are interpreted as 0.0.
|
||||
positive_label (str): The positive label for a binary task with
|
||||
exclusive classes. Defaults to None.
|
||||
threshold (float): Cutoff to consider a prediction "positive". Defaults
|
||||
|
|
@ -484,13 +485,15 @@ class Scorer:
|
|||
|
||||
for label in labels:
|
||||
pred_score = pred_cats.get(label, 0.0)
|
||||
gold_score = gold_cats.get(label, 0.0)
|
||||
gold_score = gold_cats.get(label)
|
||||
if not gold_score and not multi_label:
|
||||
gold_score = 0.0
|
||||
if gold_score is not None:
|
||||
auc_per_type[label].score_set(pred_score, gold_score)
|
||||
if multi_label:
|
||||
for label in labels:
|
||||
pred_score = pred_cats.get(label, 0.0)
|
||||
gold_score = gold_cats.get(label, 0.0)
|
||||
gold_score = gold_cats.get(label)
|
||||
if gold_score is not None:
|
||||
if pred_score >= threshold and gold_score > 0:
|
||||
f_per_type[label].tp += 1
|
||||
|
|
@ -502,16 +505,15 @@ class Scorer:
|
|||
# Get the highest-scoring for each.
|
||||
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
|
||||
gold_label, gold_score = max(gold_cats.items(), key=lambda it: it[1])
|
||||
if gold_score is not None:
|
||||
if pred_label == gold_label and pred_score >= threshold:
|
||||
f_per_type[pred_label].tp += 1
|
||||
else:
|
||||
f_per_type[gold_label].fn += 1
|
||||
if pred_score >= threshold:
|
||||
f_per_type[pred_label].fp += 1
|
||||
if pred_label == gold_label and pred_score >= threshold:
|
||||
f_per_type[pred_label].tp += 1
|
||||
else:
|
||||
f_per_type[gold_label].fn += 1
|
||||
if pred_score >= threshold:
|
||||
f_per_type[pred_label].fp += 1
|
||||
elif gold_cats:
|
||||
gold_label, gold_score = max(gold_cats, key=lambda it: it[1])
|
||||
if gold_score is not None and gold_score > 0:
|
||||
if gold_score > 0:
|
||||
f_per_type[gold_label].fn += 1
|
||||
elif pred_cats:
|
||||
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
|
||||
|
|
|
|||
|
|
@ -155,6 +155,11 @@ def fr_tokenizer():
|
|||
return get_lang_class("fr")().tokenizer
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def fr_vocab():
|
||||
return get_lang_class("fr")().vocab
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def ga_tokenizer():
|
||||
return get_lang_class("ga")().tokenizer
|
||||
|
|
@ -205,6 +210,11 @@ def it_tokenizer():
|
|||
return get_lang_class("it")().tokenizer
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def it_vocab():
|
||||
return get_lang_class("it")().vocab
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def ja_tokenizer():
|
||||
pytest.importorskip("sudachipy")
|
||||
|
|
|
|||
|
|
@ -573,6 +573,55 @@ def test_span_with_vectors(doc):
|
|||
doc.vocab.vectors = prev_vectors
|
||||
|
||||
|
||||
# fmt: off
|
||||
def test_span_comparison(doc):
|
||||
|
||||
# Identical start, end, only differ in label and kb_id
|
||||
assert Span(doc, 0, 3) == Span(doc, 0, 3)
|
||||
assert Span(doc, 0, 3, "LABEL") == Span(doc, 0, 3, "LABEL")
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") == Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
|
||||
assert Span(doc, 0, 3) != Span(doc, 0, 3, "LABEL")
|
||||
assert Span(doc, 0, 3) != Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
assert Span(doc, 0, 3, "LABEL") != Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
|
||||
assert Span(doc, 0, 3) <= Span(doc, 0, 3) and Span(doc, 0, 3) >= Span(doc, 0, 3)
|
||||
assert Span(doc, 0, 3, "LABEL") <= Span(doc, 0, 3, "LABEL") and Span(doc, 0, 3, "LABEL") >= Span(doc, 0, 3, "LABEL")
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") <= Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") >= Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
|
||||
assert (Span(doc, 0, 3) < Span(doc, 0, 3, "", kb_id="KB_ID") < Span(doc, 0, 3, "LABEL") < Span(doc, 0, 3, "LABEL", kb_id="KB_ID"))
|
||||
assert (Span(doc, 0, 3) <= Span(doc, 0, 3, "", kb_id="KB_ID") <= Span(doc, 0, 3, "LABEL") <= Span(doc, 0, 3, "LABEL", kb_id="KB_ID"))
|
||||
|
||||
assert (Span(doc, 0, 3, "LABEL", kb_id="KB_ID") > Span(doc, 0, 3, "LABEL") > Span(doc, 0, 3, "", kb_id="KB_ID") > Span(doc, 0, 3))
|
||||
assert (Span(doc, 0, 3, "LABEL", kb_id="KB_ID") >= Span(doc, 0, 3, "LABEL") >= Span(doc, 0, 3, "", kb_id="KB_ID") >= Span(doc, 0, 3))
|
||||
|
||||
# Different end
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") < Span(doc, 0, 4, "LABEL", kb_id="KB_ID")
|
||||
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") < Span(doc, 0, 4)
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") <= Span(doc, 0, 4)
|
||||
assert Span(doc, 0, 4) > Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
assert Span(doc, 0, 4) >= Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
|
||||
# Different start
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") != Span(doc, 1, 3, "LABEL", kb_id="KB_ID")
|
||||
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") < Span(doc, 1, 3)
|
||||
assert Span(doc, 0, 3, "LABEL", kb_id="KB_ID") <= Span(doc, 1, 3)
|
||||
assert Span(doc, 1, 3) > Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
assert Span(doc, 1, 3) >= Span(doc, 0, 3, "LABEL", kb_id="KB_ID")
|
||||
|
||||
# Different start & different end
|
||||
assert Span(doc, 0, 4, "LABEL", kb_id="KB_ID") != Span(doc, 1, 3, "LABEL", kb_id="KB_ID")
|
||||
|
||||
assert Span(doc, 0, 4, "LABEL", kb_id="KB_ID") < Span(doc, 1, 3)
|
||||
assert Span(doc, 0, 4, "LABEL", kb_id="KB_ID") <= Span(doc, 1, 3)
|
||||
assert Span(doc, 1, 3) > Span(doc, 0, 4, "LABEL", kb_id="KB_ID")
|
||||
assert Span(doc, 1, 3) >= Span(doc, 0, 4, "LABEL", kb_id="KB_ID")
|
||||
# fmt: on
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"start,end,expected_sentences,expected_sentences_with_hook",
|
||||
[
|
||||
|
|
|
|||
174
spacy/tests/lang/fi/test_noun_chunks.py
Normal file
174
spacy/tests/lang/fi/test_noun_chunks.py
Normal file
|
|
@ -0,0 +1,174 @@
|
|||
import pytest
|
||||
from spacy.tokens import Doc
|
||||
|
||||
|
||||
FI_NP_TEST_EXAMPLES = [
|
||||
(
|
||||
"Kaksi tyttöä potkii punaista palloa",
|
||||
["NUM", "NOUN", "VERB", "ADJ", "NOUN"],
|
||||
["nummod", "nsubj", "ROOT", "amod", "obj"],
|
||||
[1, 1, 0, 1, -2],
|
||||
["Kaksi tyttöä", "punaista palloa"],
|
||||
),
|
||||
(
|
||||
"Erittäin vaarallinen leijona karkasi kiertävän sirkuksen eläintenkesyttäjältä",
|
||||
["ADV", "ADJ", "NOUN", "VERB", "ADJ", "NOUN", "NOUN"],
|
||||
["advmod", "amod", "nsubj", "ROOT", "amod", "nmod:poss", "obl"],
|
||||
[1, 1, 1, 0, 1, 1, -3],
|
||||
["Erittäin vaarallinen leijona", "kiertävän sirkuksen eläintenkesyttäjältä"],
|
||||
),
|
||||
(
|
||||
"Leijona raidallisine tassuineen piileksii Porin kaupungin lähellä",
|
||||
["NOUN", "ADJ", "NOUN", "VERB", "PROPN", "NOUN", "ADP"],
|
||||
["nsubj", "amod", "nmod", "ROOT", "nmod:poss", "obl", "case"],
|
||||
[3, 1, -2, 0, 1, -2, -1],
|
||||
["Leijona raidallisine tassuineen", "Porin kaupungin"],
|
||||
),
|
||||
(
|
||||
"Lounaalla nautittiin salaattia, maukasta kanaa ja raikasta vettä",
|
||||
["NOUN", "VERB", "NOUN", "PUNCT", "ADJ", "NOUN", "CCONJ", "ADJ", "NOUN"],
|
||||
["obl", "ROOT", "obj", "punct", "amod", "conj", "cc", "amod", "conj"],
|
||||
[1, 0, -1, 2, 1, -3, 2, 1, -6],
|
||||
["Lounaalla", "salaattia", "maukasta kanaa", "raikasta vettä"],
|
||||
),
|
||||
(
|
||||
"Minua houkuttaa maalle muuttaminen talven jälkeen",
|
||||
["PRON", "VERB", "NOUN", "NOUN", "NOUN", "ADP"],
|
||||
["obj", "ROOT", "nmod", "nsubj", "obl", "case"],
|
||||
[1, 0, 1, -2, -3, -1],
|
||||
["maalle muuttaminen", "talven"],
|
||||
),
|
||||
(
|
||||
"Päivän kohokohta oli vierailu museossa kummilasten kanssa",
|
||||
["NOUN", "NOUN", "AUX", "NOUN", "NOUN", "NOUN", "ADP"],
|
||||
["nmod:poss", "nsubj:cop", "cop", "ROOT", "nmod", "obl", "case"],
|
||||
[1, 2, 1, 0, -1, -2, -1],
|
||||
["Päivän kohokohta", "vierailu museossa", "kummilasten"],
|
||||
),
|
||||
(
|
||||
"Yrittäjät maksoivat tuomioistuimen määräämät korvaukset",
|
||||
["NOUN", "VERB", "NOUN", "VERB", "NOUN"],
|
||||
["nsubj", "ROOT", "nsubj", "acl", "obj"],
|
||||
[1, 0, 1, 1, -3],
|
||||
["Yrittäjät", "tuomioistuimen", "korvaukset"],
|
||||
),
|
||||
(
|
||||
"Julkisoikeudelliset tai niihin rinnastettavat saatavat ovat suoraan ulosottokelpoisia",
|
||||
["ADJ", "CCONJ", "PRON", "VERB", "NOUN", "AUX", "ADV", "NOUN"],
|
||||
["amod", "cc", "obl", "acl", "nsubj:cop", "cop", "advmod", "ROOT"],
|
||||
[4, 3, 1, 1, 3, 2, 1, 0],
|
||||
["Julkisoikeudelliset tai niihin rinnastettavat saatavat", "ulosottokelpoisia"],
|
||||
),
|
||||
(
|
||||
"Se oli ala-arvoista käytöstä kaikilta oppilailta, myös valvojaoppilailta",
|
||||
["PRON", "AUX", "ADJ", "NOUN", "PRON", "NOUN", "PUNCT", "ADV", "NOUN"],
|
||||
["nsubj:cop", "cop", "amod", "ROOT", "det", "nmod", "punct", "advmod", "appos"],
|
||||
[3, 2, 1, 0, 1, -2, 2, 1, -3],
|
||||
["ala-arvoista käytöstä kaikilta oppilailta", "valvojaoppilailta"],
|
||||
),
|
||||
(
|
||||
"Isä souti veneellä, jonka hän oli vuokrannut",
|
||||
["NOUN", "VERB", "NOUN", "PUNCT", "PRON", "PRON", "AUX", "VERB"],
|
||||
["nsubj", "ROOT", "obl", "punct", "obj", "nsubj", "aux", "acl:relcl"],
|
||||
[1, 0, -1, 4, 3, 2, 1, -5],
|
||||
["Isä", "veneellä"],
|
||||
),
|
||||
(
|
||||
"Kirja, jonka poimin hyllystä, kertoo norsuista",
|
||||
["NOUN", "PUNCT", "PRON", "VERB", "NOUN", "PUNCT", "VERB", "NOUN"],
|
||||
["nsubj", "punct", "obj", "acl:relcl", "obl", "punct", "ROOT", "obl"],
|
||||
[6, 2, 1, -3, -1, 1, 0, -1],
|
||||
["Kirja", "hyllystä", "norsuista"],
|
||||
),
|
||||
(
|
||||
"Huomenna on päivä, jota olemme odottaneet",
|
||||
["NOUN", "AUX", "NOUN", "PUNCT", "PRON", "AUX", "VERB"],
|
||||
["ROOT", "cop", "nsubj:cop", "punct", "obj", "aux", "acl:relcl"],
|
||||
[0, -1, -2, 3, 2, 1, -4],
|
||||
["Huomenna", "päivä"],
|
||||
),
|
||||
(
|
||||
"Liikkuvuuden lisääminen on yksi korkeakoulutuksen keskeisistä kehittämiskohteista",
|
||||
["NOUN", "NOUN", "AUX", "PRON", "NOUN", "ADJ", "NOUN"],
|
||||
["nmod:gobj", "nsubj:cop", "cop", "ROOT", "nmod:poss", "amod", "nmod"],
|
||||
[1, 2, 1, 0, 2, 1, -3],
|
||||
[
|
||||
"Liikkuvuuden lisääminen",
|
||||
"korkeakoulutuksen keskeisistä kehittämiskohteista",
|
||||
],
|
||||
),
|
||||
(
|
||||
"Kaupalliset palvelut jätetään yksityisten palveluntarjoajien tarjottavaksi",
|
||||
["ADJ", "NOUN", "VERB", "ADJ", "NOUN", "NOUN"],
|
||||
["amod", "obj", "ROOT", "amod", "nmod:gsubj", "obl"],
|
||||
[1, 1, 0, 1, 1, -3],
|
||||
["Kaupalliset palvelut", "yksityisten palveluntarjoajien tarjottavaksi"],
|
||||
),
|
||||
(
|
||||
"New York tunnetaan kaupunkina, joka ei koskaan nuku",
|
||||
["PROPN", "PROPN", "VERB", "NOUN", "PUNCT", "PRON", "AUX", "ADV", "VERB"],
|
||||
["obj", "flat:name", "ROOT", "obl", "punct", "nsubj", "aux", "advmod", "acl:relcl"],
|
||||
[2, -1, 0, -1, 4, 3, 2, 1, -5],
|
||||
["New York", "kaupunkina"],
|
||||
),
|
||||
(
|
||||
"Loput vihjeet saat herra Möttöseltä",
|
||||
["NOUN", "NOUN", "VERB", "NOUN", "PROPN"],
|
||||
["compound:nn", "obj", "ROOT", "compound:nn", "obj"],
|
||||
[1, 1, 0, 1, -2],
|
||||
["Loput vihjeet", "herra Möttöseltä"],
|
||||
),
|
||||
(
|
||||
"mahdollisuus tukea muita päivystysyksiköitä",
|
||||
["NOUN", "VERB", "PRON", "NOUN"],
|
||||
["ROOT", "acl", "det", "obj"],
|
||||
[0, -1, 1, -2],
|
||||
["mahdollisuus", "päivystysyksiköitä"],
|
||||
),
|
||||
(
|
||||
"sairaanhoitopiirit harjoittavat leikkaustoimintaa alueellaan useammassa sairaalassa",
|
||||
["NOUN", "VERB", "NOUN", "NOUN", "ADJ", "NOUN"],
|
||||
["nsubj", "ROOT", "obj", "obl", "amod", "obl"],
|
||||
[1, 0, -1, -1, 1, -3],
|
||||
["sairaanhoitopiirit", "leikkaustoimintaa", "alueellaan", "useammassa sairaalassa"],
|
||||
),
|
||||
(
|
||||
"Lain mukaan varhaiskasvatus on suunnitelmallista toimintaa",
|
||||
["NOUN", "ADP", "NOUN", "AUX", "ADJ", "NOUN"],
|
||||
["obl", "case", "nsubj:cop", "cop", "amod", "ROOT"],
|
||||
[5, -1, 3, 2, 1, 0],
|
||||
["Lain", "varhaiskasvatus", "suunnitelmallista toimintaa"],
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
def test_noun_chunks_is_parsed(fi_tokenizer):
|
||||
"""Test that noun_chunks raises Value Error for 'fi' language if Doc is not parsed.
|
||||
To check this test, we're constructing a Doc
|
||||
with a new Vocab here and forcing is_parsed to 'False'
|
||||
to make sure the noun chunks don't run.
|
||||
"""
|
||||
doc = fi_tokenizer("Tämä on testi")
|
||||
with pytest.raises(ValueError):
|
||||
list(doc.noun_chunks)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"text,pos,deps,heads,expected_noun_chunks", FI_NP_TEST_EXAMPLES
|
||||
)
|
||||
def test_fi_noun_chunks(fi_tokenizer, text, pos, deps, heads, expected_noun_chunks):
|
||||
tokens = fi_tokenizer(text)
|
||||
|
||||
assert len(heads) == len(pos)
|
||||
doc = Doc(
|
||||
tokens.vocab,
|
||||
words=[t.text for t in tokens],
|
||||
heads=[head + i for i, head in enumerate(heads)],
|
||||
deps=deps,
|
||||
pos=pos,
|
||||
)
|
||||
|
||||
noun_chunks = list(doc.noun_chunks)
|
||||
assert len(noun_chunks) == len(expected_noun_chunks)
|
||||
for i, np in enumerate(noun_chunks):
|
||||
assert np.text == expected_noun_chunks[i]
|
||||
|
|
@ -1,8 +1,230 @@
|
|||
from spacy.tokens import Doc
|
||||
import pytest
|
||||
|
||||
|
||||
# fmt: off
|
||||
@pytest.mark.parametrize(
|
||||
"words,heads,deps,pos,chunk_offsets",
|
||||
[
|
||||
# determiner + noun
|
||||
# un nom -> un nom
|
||||
(
|
||||
["un", "nom"],
|
||||
[1, 1],
|
||||
["det", "ROOT"],
|
||||
["DET", "NOUN"],
|
||||
[(0, 2)],
|
||||
),
|
||||
# determiner + noun starting with vowel
|
||||
# l'heure -> l'heure
|
||||
(
|
||||
["l'", "heure"],
|
||||
[1, 1],
|
||||
["det", "ROOT"],
|
||||
["DET", "NOUN"],
|
||||
[(0, 2)],
|
||||
),
|
||||
# determiner + plural noun
|
||||
# les romans -> les romans
|
||||
(
|
||||
["les", "romans"],
|
||||
[1, 1],
|
||||
["det", "ROOT"],
|
||||
["DET", "NOUN"],
|
||||
[(0, 2)],
|
||||
),
|
||||
# det + adj + noun
|
||||
# Le vieux Londres -> Le vieux Londres
|
||||
(
|
||||
['Les', 'vieux', 'Londres'],
|
||||
[2, 2, 2],
|
||||
["det", "amod", "ROOT"],
|
||||
["DET", "ADJ", "NOUN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# det + noun + adj
|
||||
# le nom propre -> le nom propre a proper noun
|
||||
(
|
||||
["le", "nom", "propre"],
|
||||
[1, 1, 1],
|
||||
["det", "ROOT", "amod"],
|
||||
["DET", "NOUN", "ADJ"],
|
||||
[(0, 3)],
|
||||
),
|
||||
# det + noun + adj plural
|
||||
# Les chiens bruns -> les chiens bruns
|
||||
(
|
||||
["Les", "chiens", "bruns"],
|
||||
[1, 1, 1],
|
||||
["det", "ROOT", "amod"],
|
||||
["DET", "NOUN", "ADJ"],
|
||||
[(0, 3)],
|
||||
),
|
||||
# multiple adjectives: one adj before the noun, one adj after the noun
|
||||
# un nouveau film intéressant -> un nouveau film intéressant
|
||||
(
|
||||
["un", "nouveau", "film", "intéressant"],
|
||||
[2, 2, 2, 2],
|
||||
["det", "amod", "ROOT", "amod"],
|
||||
["DET", "ADJ", "NOUN", "ADJ"],
|
||||
[(0,4)]
|
||||
),
|
||||
# multiple adjectives, both adjs after the noun
|
||||
# une personne intelligente et drôle -> une personne intelligente et drôle
|
||||
(
|
||||
["une", "personne", "intelligente", "et", "drôle"],
|
||||
[1, 1, 1, 4, 2],
|
||||
["det", "ROOT", "amod", "cc", "conj"],
|
||||
["DET", "NOUN", "ADJ", "CCONJ", "ADJ"],
|
||||
[(0,5)]
|
||||
),
|
||||
# relative pronoun
|
||||
# un bus qui va au ville -> un bus, qui, ville
|
||||
(
|
||||
['un', 'bus', 'qui', 'va', 'au', 'ville'],
|
||||
[1, 1, 3, 1, 5, 3],
|
||||
['det', 'ROOT', 'nsubj', 'acl:relcl', 'case', 'obl:arg'],
|
||||
['DET', 'NOUN', 'PRON', 'VERB', 'ADP', 'NOUN'],
|
||||
[(0,2), (2,3), (5,6)]
|
||||
),
|
||||
# relative subclause
|
||||
# Voilà la maison que nous voulons acheter -> la maison, nous That's the house that we want to buy.
|
||||
(
|
||||
['Voilà', 'la', 'maison', 'que', 'nous', 'voulons', 'acheter'],
|
||||
[0, 2, 0, 5, 5, 2, 5],
|
||||
['ROOT', 'det', 'obj', 'mark', 'nsubj', 'acl:relcl', 'xcomp'],
|
||||
['VERB', 'DET', 'NOUN', 'SCONJ', 'PRON', 'VERB', 'VERB'],
|
||||
[(1,3), (4,5)]
|
||||
),
|
||||
# Person name and title by flat
|
||||
# Louis XIV -> Louis XIV
|
||||
(
|
||||
["Louis", "XIV"],
|
||||
[0, 0],
|
||||
["ROOT", "flat:name"],
|
||||
["PROPN", "PROPN"],
|
||||
[(0,2)]
|
||||
),
|
||||
# Organization name by flat
|
||||
# Nations Unies -> Nations Unies
|
||||
(
|
||||
["Nations", "Unies"],
|
||||
[0, 0],
|
||||
["ROOT", "flat:name"],
|
||||
["PROPN", "PROPN"],
|
||||
[(0,2)]
|
||||
),
|
||||
# Noun compound, person name created by two flats
|
||||
# Louise de Bratagne -> Louise de Bratagne
|
||||
(
|
||||
["Louise", "de", "Bratagne"],
|
||||
[0, 0, 0],
|
||||
["ROOT", "flat:name", "flat:name"],
|
||||
["PROPN", "PROPN", "PROPN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# Noun compound, person name created by two flats
|
||||
# Louis François Joseph -> Louis François Joseph
|
||||
(
|
||||
["Louis", "François", "Joseph"],
|
||||
[0, 0, 0],
|
||||
["ROOT", "flat:name", "flat:name"],
|
||||
["PROPN", "PROPN", "PROPN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# one determiner + one noun + one adjective qualified by an adverb
|
||||
# quelques agriculteurs très riches -> quelques agriculteurs très riches
|
||||
(
|
||||
["quelques", "agriculteurs", "très", "riches"],
|
||||
[1, 1, 3, 1],
|
||||
['det', 'ROOT', 'advmod', 'amod'],
|
||||
['DET', 'NOUN', 'ADV', 'ADJ'],
|
||||
[(0,4)]
|
||||
),
|
||||
# Two NPs conjuncted
|
||||
# Il a un chien et un chat -> Il, un chien, un chat
|
||||
(
|
||||
['Il', 'a', 'un', 'chien', 'et', 'un', 'chat'],
|
||||
[1, 1, 3, 1, 6, 6, 3],
|
||||
['nsubj', 'ROOT', 'det', 'obj', 'cc', 'det', 'conj'],
|
||||
['PRON', 'VERB', 'DET', 'NOUN', 'CCONJ', 'DET', 'NOUN'],
|
||||
[(0,1), (2,4), (5,7)]
|
||||
|
||||
),
|
||||
# Two NPs together
|
||||
# l'écrivain brésilien Aníbal Machado -> l'écrivain brésilien, Aníbal Machado
|
||||
(
|
||||
["l'", 'écrivain', 'brésilien', 'Aníbal', 'Machado'],
|
||||
[1, 1, 1, 1, 3],
|
||||
['det', 'ROOT', 'amod', 'appos', 'flat:name'],
|
||||
['DET', 'NOUN', 'ADJ', 'PROPN', 'PROPN'],
|
||||
[(0, 3), (3, 5)]
|
||||
),
|
||||
# nmod relation between NPs
|
||||
# la destruction de la ville -> la destruction, la ville
|
||||
(
|
||||
['la', 'destruction', 'de', 'la', 'ville'],
|
||||
[1, 1, 4, 4, 1],
|
||||
['det', 'ROOT', 'case', 'det', 'nmod'],
|
||||
['DET', 'NOUN', 'ADP', 'DET', 'NOUN'],
|
||||
[(0,2), (3,5)]
|
||||
),
|
||||
# nmod relation between NPs
|
||||
# Archiduchesse d’Autriche -> Archiduchesse, Autriche
|
||||
(
|
||||
['Archiduchesse', 'd’', 'Autriche'],
|
||||
[0, 2, 0],
|
||||
['ROOT', 'case', 'nmod'],
|
||||
['NOUN', 'ADP', 'PROPN'],
|
||||
[(0,1), (2,3)]
|
||||
),
|
||||
# Compounding by nmod, several NPs chained together
|
||||
# la première usine de drogue du gouvernement -> la première usine, drogue, gouvernement
|
||||
(
|
||||
["la", "première", "usine", "de", "drogue", "du", "gouvernement"],
|
||||
[2, 2, 2, 4, 2, 6, 2],
|
||||
['det', 'amod', 'ROOT', 'case', 'nmod', 'case', 'nmod'],
|
||||
['DET', 'ADJ', 'NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN'],
|
||||
[(0, 3), (4, 5), (6, 7)]
|
||||
),
|
||||
# several NPs
|
||||
# Traduction du rapport de Susana -> Traduction, rapport, Susana
|
||||
(
|
||||
['Traduction', 'du', 'raport', 'de', 'Susana'],
|
||||
[0, 2, 0, 4, 2],
|
||||
['ROOT', 'case', 'nmod', 'case', 'nmod'],
|
||||
['NOUN', 'ADP', 'NOUN', 'ADP', 'PROPN'],
|
||||
[(0,1), (2,3), (4,5)]
|
||||
|
||||
),
|
||||
# Several NPs
|
||||
# Le gros chat de Susana et son amie -> Le gros chat, Susana, son amie
|
||||
(
|
||||
['Le', 'gros', 'chat', 'de', 'Susana', 'et', 'son', 'amie'],
|
||||
[2, 2, 2, 4, 2, 7, 7, 2],
|
||||
['det', 'amod', 'ROOT', 'case', 'nmod', 'cc', 'det', 'conj'],
|
||||
['DET', 'ADJ', 'NOUN', 'ADP', 'PROPN', 'CCONJ', 'DET', 'NOUN'],
|
||||
[(0,3), (4,5), (6,8)]
|
||||
),
|
||||
# Passive subject
|
||||
# Les nouvelles dépenses sont alimentées par le grand compte bancaire de Clinton -> Les nouvelles dépenses, le grand compte bancaire, Clinton
|
||||
(
|
||||
['Les', 'nouvelles', 'dépenses', 'sont', 'alimentées', 'par', 'le', 'grand', 'compte', 'bancaire', 'de', 'Clinton'],
|
||||
[2, 2, 4, 4, 4, 8, 8, 8, 4, 8, 11, 8],
|
||||
['det', 'amod', 'nsubj:pass', 'aux:pass', 'ROOT', 'case', 'det', 'amod', 'obl:agent', 'amod', 'case', 'nmod'],
|
||||
['DET', 'ADJ', 'NOUN', 'AUX', 'VERB', 'ADP', 'DET', 'ADJ', 'NOUN', 'ADJ', 'ADP', 'PROPN'],
|
||||
[(0, 3), (6, 10), (11, 12)]
|
||||
)
|
||||
],
|
||||
)
|
||||
# fmt: on
|
||||
def test_fr_noun_chunks(fr_vocab, words, heads, deps, pos, chunk_offsets):
|
||||
doc = Doc(fr_vocab, words=words, heads=heads, deps=deps, pos=pos)
|
||||
assert [(c.start, c.end) for c in doc.noun_chunks] == chunk_offsets
|
||||
|
||||
|
||||
def test_noun_chunks_is_parsed_fr(fr_tokenizer):
|
||||
"""Test that noun_chunks raises Value Error for 'fr' language if Doc is not parsed."""
|
||||
doc = fr_tokenizer("trouver des travaux antérieurs")
|
||||
doc = fr_tokenizer("Je suis allé à l'école")
|
||||
with pytest.raises(ValueError):
|
||||
list(doc.noun_chunks)
|
||||
|
|
|
|||
221
spacy/tests/lang/it/test_noun_chunks.py
Normal file
221
spacy/tests/lang/it/test_noun_chunks.py
Normal file
|
|
@ -0,0 +1,221 @@
|
|||
from spacy.tokens import Doc
|
||||
import pytest
|
||||
|
||||
|
||||
# fmt: off
|
||||
@pytest.mark.parametrize(
|
||||
"words,heads,deps,pos,chunk_offsets",
|
||||
[
|
||||
# determiner + noun
|
||||
# un pollo -> un pollo
|
||||
(
|
||||
["un", "pollo"],
|
||||
[1, 1],
|
||||
["det", "ROOT"],
|
||||
["DET", "NOUN"],
|
||||
[(0,2)],
|
||||
),
|
||||
# two determiners + noun
|
||||
# il mio cane -> il mio cane
|
||||
(
|
||||
["il", "mio", "cane"],
|
||||
[2, 2, 2],
|
||||
["det", "det:poss", "ROOT"],
|
||||
["DET", "DET", "NOUN"],
|
||||
[(0,3)],
|
||||
),
|
||||
# two determiners, one is after noun. rare usage but still testing
|
||||
# il cane mio-> il cane mio
|
||||
(
|
||||
["il", "cane", "mio"],
|
||||
[1, 1, 1],
|
||||
["det", "ROOT", "det:poss"],
|
||||
["DET", "NOUN", "DET"],
|
||||
[(0,3)],
|
||||
),
|
||||
# relative pronoun
|
||||
# È molto bello il vestito che hai acquistat -> il vestito, che the dress that you bought is very pretty.
|
||||
(
|
||||
["È", "molto", "bello", "il", "vestito", "che", "hai", "acquistato"],
|
||||
[2, 2, 2, 4, 2, 7, 7, 4],
|
||||
['cop', 'advmod', 'ROOT', 'det', 'nsubj', 'obj', 'aux', 'acl:relcl'],
|
||||
['AUX', 'ADV', 'ADJ', 'DET', 'NOUN', 'PRON', 'AUX', 'VERB'],
|
||||
[(3,5), (5,6)]
|
||||
),
|
||||
# relative subclause
|
||||
# il computer che hai comprato -> il computer, che the computer that you bought
|
||||
(
|
||||
['il', 'computer', 'che', 'hai', 'comprato'],
|
||||
[1, 1, 4, 4, 1],
|
||||
['det', 'ROOT', 'nsubj', 'aux', 'acl:relcl'],
|
||||
['DET', 'NOUN', 'PRON', 'AUX', 'VERB'],
|
||||
[(0,2), (2,3)]
|
||||
),
|
||||
# det + noun + adj
|
||||
# Una macchina grande -> Una macchina grande
|
||||
(
|
||||
["Una", "macchina", "grande"],
|
||||
[1, 1, 1],
|
||||
["det", "ROOT", "amod"],
|
||||
["DET", "NOUN", "ADJ"],
|
||||
[(0,3)],
|
||||
),
|
||||
# noun + adj plural
|
||||
# mucche bianche
|
||||
(
|
||||
["mucche", "bianche"],
|
||||
[0, 0],
|
||||
["ROOT", "amod"],
|
||||
["NOUN", "ADJ"],
|
||||
[(0,2)],
|
||||
),
|
||||
# det + adj + noun
|
||||
# Una grande macchina -> Una grande macchina
|
||||
(
|
||||
['Una', 'grande', 'macchina'],
|
||||
[2, 2, 2],
|
||||
["det", "amod", "ROOT"],
|
||||
["DET", "ADJ", "NOUN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# det + adj + noun, det with apostrophe
|
||||
# un'importante associazione -> un'importante associazione
|
||||
(
|
||||
["Un'", 'importante', 'associazione'],
|
||||
[2, 2, 2],
|
||||
["det", "amod", "ROOT"],
|
||||
["DET", "ADJ", "NOUN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# multiple adjectives
|
||||
# Un cane piccolo e marrone -> Un cane piccolo e marrone
|
||||
(
|
||||
["Un", "cane", "piccolo", "e", "marrone"],
|
||||
[1, 1, 1, 4, 2],
|
||||
["det", "ROOT", "amod", "cc", "conj"],
|
||||
["DET", "NOUN", "ADJ", "CCONJ", "ADJ"],
|
||||
[(0,5)]
|
||||
),
|
||||
# determiner, adjective, compound created by flat
|
||||
# le Nazioni Unite -> le Nazioni Unite
|
||||
(
|
||||
["le", "Nazioni", "Unite"],
|
||||
[1, 1, 1],
|
||||
["det", "ROOT", "flat:name"],
|
||||
["DET", "PROPN", "PROPN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# one determiner + one noun + one adjective qualified by an adverb
|
||||
# alcuni contadini molto ricchi -> alcuni contadini molto ricchi some very rich farmers
|
||||
(
|
||||
['alcuni', 'contadini', 'molto', 'ricchi'],
|
||||
[1, 1, 3, 1],
|
||||
['det', 'ROOT', 'advmod', 'amod'],
|
||||
['DET', 'NOUN', 'ADV', 'ADJ'],
|
||||
[(0,4)]
|
||||
),
|
||||
# Two NPs conjuncted
|
||||
# Ho un cane e un gatto -> un cane, un gatto
|
||||
(
|
||||
['Ho', 'un', 'cane', 'e', 'un', 'gatto'],
|
||||
[0, 2, 0, 5, 5, 0],
|
||||
['ROOT', 'det', 'obj', 'cc', 'det', 'conj'],
|
||||
['VERB', 'DET', 'NOUN', 'CCONJ', 'DET', 'NOUN'],
|
||||
[(1,3), (4,6)]
|
||||
|
||||
),
|
||||
# Two NPs together
|
||||
# lo scrittore brasiliano Aníbal Machado -> lo scrittore brasiliano, Aníbal Machado
|
||||
(
|
||||
['lo', 'scrittore', 'brasiliano', 'Aníbal', 'Machado'],
|
||||
[1, 1, 1, 1, 3],
|
||||
['det', 'ROOT', 'amod', 'nmod', 'flat:name'],
|
||||
['DET', 'NOUN', 'ADJ', 'PROPN', 'PROPN'],
|
||||
[(0, 3), (3, 5)]
|
||||
),
|
||||
# Noun compound, person name and titles
|
||||
# Dom Pedro II -> Dom Pedro II
|
||||
(
|
||||
["Dom", "Pedro", "II"],
|
||||
[0, 0, 0],
|
||||
["ROOT", "flat:name", "flat:name"],
|
||||
["PROPN", "PROPN", "PROPN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# Noun compound created by flat
|
||||
# gli Stati Uniti
|
||||
(
|
||||
["gli", "Stati", "Uniti"],
|
||||
[1, 1, 1],
|
||||
["det", "ROOT", "flat:name"],
|
||||
["DET", "PROPN", "PROPN"],
|
||||
[(0,3)]
|
||||
),
|
||||
# nmod relation between NPs
|
||||
# la distruzione della città -> la distruzione, città
|
||||
(
|
||||
['la', 'distruzione', 'della', 'città'],
|
||||
[1, 1, 3, 1],
|
||||
['det', 'ROOT', 'case', 'nmod'],
|
||||
['DET', 'NOUN', 'ADP', 'NOUN'],
|
||||
[(0,2), (3,4)]
|
||||
),
|
||||
# Compounding by nmod, several NPs chained together
|
||||
# la prima fabbrica di droga del governo -> la prima fabbrica, droga, governo
|
||||
(
|
||||
["la", "prima", "fabbrica", "di", "droga", "del", "governo"],
|
||||
[2, 2, 2, 4, 2, 6, 2],
|
||||
['det', 'amod', 'ROOT', 'case', 'nmod', 'case', 'nmod'],
|
||||
['DET', 'ADJ', 'NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN'],
|
||||
[(0, 3), (4, 5), (6, 7)]
|
||||
),
|
||||
# several NPs
|
||||
# Traduzione del rapporto di Susana -> Traduzione, rapporto, Susana
|
||||
(
|
||||
['Traduzione', 'del', 'rapporto', 'di', 'Susana'],
|
||||
[0, 2, 0, 4, 2],
|
||||
['ROOT', 'case', 'nmod', 'case', 'nmod'],
|
||||
['NOUN', 'ADP', 'NOUN', 'ADP', 'PROPN'],
|
||||
[(0,1), (2,3), (4,5)]
|
||||
|
||||
),
|
||||
# Several NPs
|
||||
# Il gatto grasso di Susana e la sua amica -> Il gatto grasso, Susana, sua amica
|
||||
(
|
||||
['Il', 'gatto', 'grasso', 'di', 'Susana', 'e', 'la', 'sua', 'amica'],
|
||||
[1, 1, 1, 4, 1, 8, 8, 8, 1],
|
||||
['det', 'ROOT', 'amod', 'case', 'nmod', 'cc', 'det', 'det:poss', 'conj'],
|
||||
['DET', 'NOUN', 'ADJ', 'ADP', 'PROPN', 'CCONJ', 'DET', 'DET', 'NOUN'],
|
||||
[(0,3), (4,5), (6,9)]
|
||||
),
|
||||
# Passive subject
|
||||
# La nuova spesa è alimentata dal grande conto in banca di Clinton -> Le nuova spesa, grande conto, banca, Clinton
|
||||
(
|
||||
['La', 'nuova', 'spesa', 'è', 'alimentata', 'dal', 'grande', 'conto', 'in', 'banca', 'di', 'Clinton'],
|
||||
[2, 2, 4, 4, 4, 7, 7, 4, 9, 7, 11, 9],
|
||||
['det', 'amod', 'nsubj:pass', 'aux:pass', 'ROOT', 'case', 'amod', 'obl:agent', 'case', 'nmod', 'case', 'nmod'],
|
||||
['DET', 'ADJ', 'NOUN', 'AUX', 'VERB', 'ADP', 'ADJ', 'NOUN', 'ADP', 'NOUN', 'ADP', 'PROPN'],
|
||||
[(0, 3), (6, 8), (9, 10), (11,12)]
|
||||
),
|
||||
# Misc
|
||||
# Ma mentre questo prestito possa ora sembrare gestibile, un improvviso cambiamento delle circostanze potrebbe portare a problemi di debiti -> questo prestiti, un provisso cambiento, circostanze, problemi, debiti
|
||||
(
|
||||
['Ma', 'mentre', 'questo', 'prestito', 'possa', 'ora', 'sembrare', 'gestibile', ',', 'un', 'improvviso', 'cambiamento', 'delle', 'circostanze', 'potrebbe', 'portare', 'a', 'problemi', 'di', 'debitii'],
|
||||
[15, 6, 3, 6, 6, 6, 15, 6, 6, 11, 11, 15, 13, 11, 15, 15, 17, 15, 19, 17],
|
||||
['cc', 'mark', 'det', 'nsubj', 'aux', 'advmod', 'advcl', 'xcomp', 'punct', 'det', 'amod', 'nsubj', 'case', 'nmod', 'aux', 'ROOT', 'case', 'obl', 'case', 'nmod'],
|
||||
['CCONJ', 'SCONJ', 'DET', 'NOUN', 'AUX', 'ADV', 'VERB', 'ADJ', 'PUNCT', 'DET', 'ADJ', 'NOUN', 'ADP', 'NOUN', 'AUX', 'VERB', 'ADP', 'NOUN', 'ADP', 'NOUN'],
|
||||
[(2,4), (9,12), (13,14), (17,18), (19,20)]
|
||||
)
|
||||
],
|
||||
)
|
||||
# fmt: on
|
||||
def test_it_noun_chunks(it_vocab, words, heads, deps, pos, chunk_offsets):
|
||||
doc = Doc(it_vocab, words=words, heads=heads, deps=deps, pos=pos)
|
||||
assert [(c.start, c.end) for c in doc.noun_chunks] == chunk_offsets
|
||||
|
||||
|
||||
def test_noun_chunks_is_parsed_it(it_tokenizer):
|
||||
"""Test that noun_chunks raises Value Error for 'it' language if Doc is not parsed."""
|
||||
doc = it_tokenizer("Sei andato a Oxford")
|
||||
with pytest.raises(ValueError):
|
||||
list(doc.noun_chunks)
|
||||
17
spacy/tests/lang/it/test_stopwords.py
Normal file
17
spacy/tests/lang/it/test_stopwords.py
Normal file
|
|
@ -0,0 +1,17 @@
|
|||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"word", ["un", "lo", "dell", "dall", "si", "ti", "mi", "quest", "quel", "quello"]
|
||||
)
|
||||
def test_stopwords_basic(it_tokenizer, word):
|
||||
tok = it_tokenizer(word)[0]
|
||||
assert tok.is_stop
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"word", ["quest'uomo", "l'ho", "un'amica", "dell'olio", "s'arrende", "m'ascolti"]
|
||||
)
|
||||
def test_stopwords_elided(it_tokenizer, word):
|
||||
tok = it_tokenizer(word)[0]
|
||||
assert tok.is_stop
|
||||
|
|
@ -277,6 +277,21 @@ def test_issue7019():
|
|||
print_prf_per_type(msg, scores, name="foo", type="bar")
|
||||
|
||||
|
||||
@pytest.mark.issue(9904)
|
||||
def test_issue9904():
|
||||
nlp = Language()
|
||||
textcat = nlp.add_pipe("textcat")
|
||||
get_examples = make_get_examples_single_label(nlp)
|
||||
nlp.initialize(get_examples)
|
||||
|
||||
examples = get_examples()
|
||||
scores = textcat.predict([eg.predicted for eg in examples])
|
||||
|
||||
loss = textcat.get_loss(examples, scores)[0]
|
||||
loss_double_bs = textcat.get_loss(examples * 2, scores.repeat(2, axis=0))[0]
|
||||
assert loss == pytest.approx(loss_double_bs)
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Test is flakey when run with others")
|
||||
def test_simple_train():
|
||||
nlp = Language()
|
||||
|
|
@ -725,6 +740,72 @@ def test_textcat_evaluation():
|
|||
assert scores["cats_micro_r"] == 4 / 6
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"multi_label,spring_p",
|
||||
[(True, 1 / 1), (False, 1 / 2)],
|
||||
)
|
||||
def test_textcat_eval_missing(multi_label: bool, spring_p: float):
|
||||
"""
|
||||
multi-label: the missing 'spring' in gold_doc_2 doesn't incur a penalty
|
||||
exclusive labels: the missing 'spring' in gold_doc_2 is interpreted as 0.0"""
|
||||
train_examples = []
|
||||
nlp = English()
|
||||
|
||||
ref1 = nlp("one")
|
||||
ref1.cats = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
||||
pred1 = nlp("one")
|
||||
pred1.cats = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
||||
train_examples.append(Example(ref1, pred1))
|
||||
|
||||
ref2 = nlp("two")
|
||||
# reference 'spring' is missing, pred 'spring' is 1
|
||||
ref2.cats = {"winter": 0.0, "summer": 0.0, "autumn": 1.0}
|
||||
pred2 = nlp("two")
|
||||
pred2.cats = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
||||
train_examples.append(Example(pred2, ref2))
|
||||
|
||||
scores = Scorer().score_cats(
|
||||
train_examples,
|
||||
"cats",
|
||||
labels=["winter", "summer", "spring", "autumn"],
|
||||
multi_label=multi_label,
|
||||
)
|
||||
assert scores["cats_f_per_type"]["spring"]["p"] == spring_p
|
||||
assert scores["cats_f_per_type"]["spring"]["r"] == 1 / 1
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"multi_label,expected_loss",
|
||||
[(True, 0), (False, 0.125)],
|
||||
)
|
||||
def test_textcat_loss(multi_label: bool, expected_loss: float):
|
||||
"""
|
||||
multi-label: the missing 'spring' in gold_doc_2 doesn't incur an increase in loss
|
||||
exclusive labels: the missing 'spring' in gold_doc_2 is interpreted as 0.0 and adds to the loss"""
|
||||
train_examples = []
|
||||
nlp = English()
|
||||
|
||||
doc1 = nlp("one")
|
||||
cats1 = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
||||
train_examples.append(Example.from_dict(doc1, {"cats": cats1}))
|
||||
|
||||
doc2 = nlp("two")
|
||||
cats2 = {"winter": 0.0, "summer": 0.0, "autumn": 1.0}
|
||||
train_examples.append(Example.from_dict(doc2, {"cats": cats2}))
|
||||
|
||||
if multi_label:
|
||||
textcat = nlp.add_pipe("textcat_multilabel")
|
||||
else:
|
||||
textcat = nlp.add_pipe("textcat")
|
||||
textcat.initialize(lambda: train_examples)
|
||||
assert isinstance(textcat, TextCategorizer)
|
||||
scores = textcat.model.ops.asarray(
|
||||
[[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype="f" # type: ignore
|
||||
)
|
||||
loss, d_scores = textcat.get_loss(train_examples, scores)
|
||||
assert loss == expected_loss
|
||||
|
||||
|
||||
def test_textcat_threshold():
|
||||
# Ensure the scorer can be called with a different threshold
|
||||
nlp = English()
|
||||
|
|
|
|||
|
|
@ -421,3 +421,13 @@ def test_Example_missing_heads():
|
|||
# Ensure that the missing head doesn't create an artificial new sentence start
|
||||
expected = [True, False, False, False, False, False]
|
||||
assert example.get_aligned_sent_starts() == expected
|
||||
|
||||
|
||||
def test_Example_aligned_whitespace(en_vocab):
|
||||
words = ["a", " ", "b"]
|
||||
tags = ["A", "SPACE", "B"]
|
||||
predicted = Doc(en_vocab, words=words)
|
||||
reference = Doc(en_vocab, words=words, tags=tags)
|
||||
|
||||
example = Example(predicted, reference)
|
||||
assert example.get_aligned("TAG", as_string=True) == tags
|
||||
|
|
|
|||
|
|
@ -126,38 +126,26 @@ cdef class Span:
|
|||
return False
|
||||
else:
|
||||
return True
|
||||
self_tuple = (self.c.start_char, self.c.end_char, self.c.label, self.c.kb_id, self.doc)
|
||||
other_tuple = (other.c.start_char, other.c.end_char, other.c.label, other.c.kb_id, other.doc)
|
||||
# <
|
||||
if op == 0:
|
||||
return self.c.start_char < other.c.start_char
|
||||
return self_tuple < other_tuple
|
||||
# <=
|
||||
elif op == 1:
|
||||
return self.c.start_char <= other.c.start_char
|
||||
return self_tuple <= other_tuple
|
||||
# ==
|
||||
elif op == 2:
|
||||
# Do the cheap comparisons first
|
||||
return (
|
||||
(self.c.start_char == other.c.start_char) and \
|
||||
(self.c.end_char == other.c.end_char) and \
|
||||
(self.c.label == other.c.label) and \
|
||||
(self.c.kb_id == other.c.kb_id) and \
|
||||
(self.doc == other.doc)
|
||||
)
|
||||
return self_tuple == other_tuple
|
||||
# !=
|
||||
elif op == 3:
|
||||
# Do the cheap comparisons first
|
||||
return not (
|
||||
(self.c.start_char == other.c.start_char) and \
|
||||
(self.c.end_char == other.c.end_char) and \
|
||||
(self.c.label == other.c.label) and \
|
||||
(self.c.kb_id == other.c.kb_id) and \
|
||||
(self.doc == other.doc)
|
||||
)
|
||||
return self_tuple != other_tuple
|
||||
# >
|
||||
elif op == 4:
|
||||
return self.c.start_char > other.c.start_char
|
||||
return self_tuple > other_tuple
|
||||
# >=
|
||||
elif op == 5:
|
||||
return self.c.start_char >= other.c.start_char
|
||||
return self_tuple >= other_tuple
|
||||
|
||||
def __hash__(self):
|
||||
return hash((self.doc, self.c.start_char, self.c.end_char, self.c.label, self.c.kb_id))
|
||||
|
|
|
|||
|
|
@ -188,6 +188,7 @@ def conllu_sentence_to_doc(
|
|||
id_ = int(id_) - 1
|
||||
head = (int(head) - 1) if head not in ("0", "_") else id_
|
||||
tag = pos if tag == "_" else tag
|
||||
pos = pos if pos != "_" else ""
|
||||
morph = morph if morph != "_" else ""
|
||||
dep = "ROOT" if dep == "root" else dep
|
||||
lemmas.append(lemma)
|
||||
|
|
|
|||
|
|
@ -159,20 +159,17 @@ cdef class Example:
|
|||
gold_values = self.reference.to_array([field])
|
||||
output = [None] * len(self.predicted)
|
||||
for token in self.predicted:
|
||||
if token.is_space:
|
||||
values = gold_values[align[token.i].dataXd]
|
||||
values = values.ravel()
|
||||
if len(values) == 0:
|
||||
output[token.i] = None
|
||||
elif len(values) == 1:
|
||||
output[token.i] = values[0]
|
||||
elif len(set(list(values))) == 1:
|
||||
# If all aligned tokens have the same value, use it.
|
||||
output[token.i] = values[0]
|
||||
else:
|
||||
values = gold_values[align[token.i].dataXd]
|
||||
values = values.ravel()
|
||||
if len(values) == 0:
|
||||
output[token.i] = None
|
||||
elif len(values) == 1:
|
||||
output[token.i] = values[0]
|
||||
elif len(set(list(values))) == 1:
|
||||
# If all aligned tokens have the same value, use it.
|
||||
output[token.i] = values[0]
|
||||
else:
|
||||
output[token.i] = None
|
||||
output[token.i] = None
|
||||
if as_string and field not in ["ENT_IOB", "SENT_START"]:
|
||||
output = [vocab.strings[o] if o is not None else o for o in output]
|
||||
return output
|
||||
|
|
|
|||
|
|
@ -34,7 +34,11 @@ only.
|
|||
Predictions will be saved to `doc.cats` as a dictionary, where the key is the
|
||||
name of the category and the value is a score between 0 and 1 (inclusive). For
|
||||
`textcat` (exclusive categories), the scores will sum to 1, while for
|
||||
`textcat_multilabel` there is no particular guarantee about their sum.
|
||||
`textcat_multilabel` there is no particular guarantee about their sum. This also
|
||||
means that for `textcat`, missing values are equated to a value of 0 (i.e.
|
||||
`False`) and are counted as such towards the loss and scoring metrics. This is
|
||||
not the case for `textcat_multilabel`, where missing values in the gold standard
|
||||
data do not influence the loss or accuracy calculations.
|
||||
|
||||
Note that when assigning values to create training data, the score of each
|
||||
category must be 0 or 1. Using other values, for example to create a document
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user