mirror of
https://github.com/explosion/spaCy.git
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1064 lines
39 KiB
Python
1064 lines
39 KiB
Python
import math
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import os
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from collections import Counter
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from pathlib import Path
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from typing import Any, Dict, List, Tuple
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import pytest
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import srsly
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from click import NoSuchOption
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from packaging.specifiers import SpecifierSet
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from thinc.api import Config
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import spacy
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from spacy import about
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from spacy.cli import info
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from spacy.cli._util import parse_config_overrides, string_to_list, walk_directory
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from spacy.cli.apply import apply
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from spacy.cli.debug_data import (
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_compile_gold,
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_get_distribution,
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_get_kl_divergence,
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_get_labels_from_model,
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_get_labels_from_spancat,
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_get_span_characteristics,
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_get_spans_length_freq_dist,
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_print_span_characteristics,
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)
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from spacy.cli.download import get_compatibility, get_version
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from spacy.cli.evaluate import render_parses
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from spacy.cli.find_threshold import find_threshold
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from spacy.cli.init_config import RECOMMENDATIONS, fill_config, init_config
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from spacy.cli.init_pipeline import _init_labels
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from spacy.cli.package import _is_permitted_package_name, get_third_party_dependencies
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from spacy.cli.validate import get_model_pkgs
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from spacy.lang.en import English
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from spacy.lang.nl import Dutch
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from spacy.language import Language
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from spacy.schemas import RecommendationSchema
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from spacy.tokens import Doc, DocBin
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from spacy.tokens.span import Span
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from spacy.training import Example, docs_to_json, offsets_to_biluo_tags
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from spacy.training.converters import conll_ner_to_docs, conllu_to_docs, iob_to_docs
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from spacy.util import ENV_VARS, get_minor_version, load_config, load_model_from_config
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from .util import make_tempdir
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@pytest.mark.issue(4665)
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def test_cli_converters_conllu_empty_heads_ner():
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"""
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conllu_to_docs should not raise an exception if the HEAD column contains an
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underscore
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"""
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input_data = """
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1 [ _ PUNCT -LRB- _ _ punct _ _
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2 This _ DET DT _ _ det _ _
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3 killing _ NOUN NN _ _ nsubj _ _
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4 of _ ADP IN _ _ case _ _
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5 a _ DET DT _ _ det _ _
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6 respected _ ADJ JJ _ _ amod _ _
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7 cleric _ NOUN NN _ _ nmod _ _
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8 will _ AUX MD _ _ aux _ _
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9 be _ AUX VB _ _ aux _ _
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10 causing _ VERB VBG _ _ root _ _
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11 us _ PRON PRP _ _ iobj _ _
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12 trouble _ NOUN NN _ _ dobj _ _
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13 for _ ADP IN _ _ case _ _
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14 years _ NOUN NNS _ _ nmod _ _
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15 to _ PART TO _ _ mark _ _
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16 come _ VERB VB _ _ acl _ _
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17 . _ PUNCT . _ _ punct _ _
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18 ] _ PUNCT -RRB- _ _ punct _ _
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"""
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docs = list(conllu_to_docs(input_data))
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# heads are all 0
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assert not all([t.head.i for t in docs[0]])
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# NER is unset
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assert not docs[0].has_annotation("ENT_IOB")
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@pytest.mark.issue(4924)
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def test_issue4924():
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nlp = Language()
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example = Example.from_dict(nlp.make_doc(""), {})
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nlp.evaluate([example])
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@pytest.mark.issue(7055)
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def test_issue7055():
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"""Test that fill-config doesn't turn sourced components into factories."""
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source_cfg = {
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"nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger"]},
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"components": {
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"tok2vec": {"factory": "tok2vec"},
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"tagger": {"factory": "tagger"},
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},
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}
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source_nlp = English.from_config(source_cfg)
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with make_tempdir() as dir_path:
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# We need to create a loadable source pipeline
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source_path = dir_path / "test_model"
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source_nlp.to_disk(source_path)
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base_cfg = {
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"nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]},
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"components": {
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"tok2vec": {"source": str(source_path)},
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"tagger": {"source": str(source_path)},
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"ner": {"factory": "ner"},
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},
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}
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base_cfg = Config(base_cfg)
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base_path = dir_path / "base.cfg"
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base_cfg.to_disk(base_path)
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output_path = dir_path / "config.cfg"
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fill_config(output_path, base_path, silent=True)
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filled_cfg = load_config(output_path)
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assert filled_cfg["components"]["tok2vec"]["source"] == str(source_path)
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assert filled_cfg["components"]["tagger"]["source"] == str(source_path)
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assert filled_cfg["components"]["ner"]["factory"] == "ner"
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assert "model" in filled_cfg["components"]["ner"]
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@pytest.mark.issue(12566)
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@pytest.mark.parametrize(
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"factory,output_file",
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[("deps", "parses.html"), ("ents", "entities.html"), ("spans", "spans.html")],
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)
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def test_issue12566(factory: str, output_file: str):
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"""
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Test if all displaCy types (ents, dep, spans) produce an HTML file
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"""
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with make_tempdir() as tmp_dir:
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# Create sample spaCy file
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doc_json = {
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"ents": [
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{"end": 54, "label": "nam_adj_country", "start": 44},
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{"end": 83, "label": "nam_liv_person", "start": 69},
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{"end": 100, "label": "nam_pro_title_book", "start": 86},
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],
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"spans": {
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"sc": [
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{"end": 54, "kb_id": "", "label": "nam_adj_country", "start": 44},
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{"end": 83, "kb_id": "", "label": "nam_liv_person", "start": 69},
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{
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"end": 100,
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"kb_id": "",
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"label": "nam_pro_title_book",
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"start": 86,
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},
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]
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},
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"text": "Niedawno czytał em nową książkę znakomitego szkockiego medioznawcy , "
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"Briana McNaira - Cultural Chaos .",
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"tokens": [
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# fmt: off
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{"id": 0, "start": 0, "end": 8, "tag": "ADV", "pos": "ADV", "morph": "Degree=Pos", "lemma": "niedawno", "dep": "advmod", "head": 1, },
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{"id": 1, "start": 9, "end": 15, "tag": "PRAET", "pos": "VERB", "morph": "Animacy=Hum|Aspect=Imp|Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act", "lemma": "czytać", "dep": "ROOT", "head": 1, },
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{"id": 2, "start": 16, "end": 18, "tag": "AGLT", "pos": "NOUN", "morph": "Animacy=Inan|Case=Ins|Gender=Masc|Number=Sing", "lemma": "em", "dep": "iobj", "head": 1, },
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{"id": 3, "start": 19, "end": 23, "tag": "ADJ", "pos": "ADJ", "morph": "Case=Acc|Degree=Pos|Gender=Fem|Number=Sing", "lemma": "nowy", "dep": "amod", "head": 4, },
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{"id": 4, "start": 24, "end": 31, "tag": "SUBST", "pos": "NOUN", "morph": "Case=Acc|Gender=Fem|Number=Sing", "lemma": "książka", "dep": "obj", "head": 1, },
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{"id": 5, "start": 32, "end": 43, "tag": "ADJ", "pos": "ADJ", "morph": "Animacy=Nhum|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing", "lemma": "znakomit", "dep": "acl", "head": 4, },
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{"id": 6, "start": 44, "end": 54, "tag": "ADJ", "pos": "ADJ", "morph": "Animacy=Hum|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing", "lemma": "szkockiy", "dep": "amod", "head": 7, },
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{"id": 7, "start": 55, "end": 66, "tag": "SUBST", "pos": "NOUN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "medioznawca", "dep": "iobj", "head": 5, },
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{"id": 8, "start": 67, "end": 68, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Comm", "lemma": ",", "dep": "punct", "head": 9, },
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{"id": 9, "start": 69, "end": 75, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "Brian", "dep": "nmod", "head": 4, },
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{"id": 10, "start": 76, "end": 83, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "McNair", "dep": "flat", "head": 9, },
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{"id": 11, "start": 84, "end": 85, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Dash", "lemma": "-", "dep": "punct", "head": 12, },
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{"id": 12, "start": 86, "end": 94, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing", "lemma": "Cultural", "dep": "conj", "head": 4, },
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{"id": 13, "start": 95, "end": 100, "tag": "SUBST", "pos": "NOUN", "morph": "Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing", "lemma": "Chaos", "dep": "flat", "head": 12, },
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{"id": 14, "start": 101, "end": 102, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Peri", "lemma": ".", "dep": "punct", "head": 1, },
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# fmt: on
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],
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}
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# Create a .spacy file
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nlp = spacy.blank("pl")
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doc = Doc(nlp.vocab).from_json(doc_json)
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# Run the evaluate command and check if the html files exist
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render_parses(
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docs=[doc], output_path=tmp_dir, model_name="", limit=1, **{factory: True}
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)
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assert (tmp_dir / output_file).is_file()
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def test_cli_info():
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nlp = Dutch()
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nlp.add_pipe("textcat")
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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raw_data = info(tmp_dir, exclude=[""])
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assert raw_data["lang"] == "nl"
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assert raw_data["components"] == ["textcat"]
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def test_cli_converters_conllu_to_docs():
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# from NorNE: https://github.com/ltgoslo/norne/blob/3d23274965f513f23aa48455b28b1878dad23c05/ud/nob/no_bokmaal-ud-dev.conllu
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lines = [
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"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tO",
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"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tB-PER",
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"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tI-PER",
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"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tO",
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]
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input_data = "\n".join(lines)
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converted_docs = list(conllu_to_docs(input_data, n_sents=1))
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assert len(converted_docs) == 1
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converted = [docs_to_json(converted_docs)]
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
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sent = converted[0]["paragraphs"][0]["sentences"][0]
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assert len(sent["tokens"]) == 4
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tokens = sent["tokens"]
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assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår"]
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assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB"]
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assert [t["head"] for t in tokens] == [1, 2, -1, 0]
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assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT"]
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ent_offsets = [
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(e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"]
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]
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biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O")
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assert biluo_tags == ["O", "B-PER", "L-PER", "O"]
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@pytest.mark.parametrize(
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"lines",
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[
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(
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"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O",
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"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|name=B-PER",
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"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tname=I-PER",
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"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O",
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"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=B-BAD",
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),
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(
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"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\t_",
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"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|NE=B-PER",
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"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tNE=L-PER",
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"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No",
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"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tNE=B-BAD",
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),
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],
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)
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def test_cli_converters_conllu_to_docs_name_ner_map(lines):
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input_data = "\n".join(lines)
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converted_docs = list(
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conllu_to_docs(input_data, n_sents=1, ner_map={"PER": "PERSON", "BAD": ""})
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)
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assert len(converted_docs) == 1
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converted = [docs_to_json(converted_docs)]
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert converted[0]["paragraphs"][0]["raw"] == "Dommer FinnEilertsen avstår. "
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
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sent = converted[0]["paragraphs"][0]["sentences"][0]
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assert len(sent["tokens"]) == 5
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tokens = sent["tokens"]
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assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår", "."]
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assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB", "PUNCT"]
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assert [t["head"] for t in tokens] == [1, 2, -1, 0, -1]
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assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT", "punct"]
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ent_offsets = [
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(e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"]
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]
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biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O")
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assert biluo_tags == ["O", "B-PERSON", "L-PERSON", "O", "O"]
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def test_cli_converters_conllu_to_docs_subtokens():
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# https://raw.githubusercontent.com/ohenrik/nb_news_ud_sm/master/original_data/no-ud-dev-ner.conllu
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lines = [
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"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O",
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"2-3\tFE\t_\t_\t_\t_\t_\t_\t_\t_",
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"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tname=B-PER",
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"3\tEilertsen\tEilertsen\tX\t_\tGender=Fem|Tense=past\t2\tname\t_\tname=I-PER",
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"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O",
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"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=O",
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]
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input_data = "\n".join(lines)
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converted_docs = list(
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conllu_to_docs(
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input_data, n_sents=1, merge_subtokens=True, append_morphology=True
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)
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)
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assert len(converted_docs) == 1
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converted = [docs_to_json(converted_docs)]
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert converted[0]["paragraphs"][0]["raw"] == "Dommer FE avstår. "
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
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sent = converted[0]["paragraphs"][0]["sentences"][0]
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assert len(sent["tokens"]) == 4
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tokens = sent["tokens"]
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assert [t["orth"] for t in tokens] == ["Dommer", "FE", "avstår", "."]
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assert [t["tag"] for t in tokens] == [
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"NOUN__Definite=Ind|Gender=Masc|Number=Sing",
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"PROPN_X__Gender=Fem,Masc|Tense=past",
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"VERB__Mood=Ind|Tense=Pres|VerbForm=Fin",
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"PUNCT",
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]
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assert [t["pos"] for t in tokens] == ["NOUN", "PROPN", "VERB", "PUNCT"]
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assert [t["morph"] for t in tokens] == [
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"Definite=Ind|Gender=Masc|Number=Sing",
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"Gender=Fem,Masc|Tense=past",
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"Mood=Ind|Tense=Pres|VerbForm=Fin",
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"",
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]
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assert [t["lemma"] for t in tokens] == ["dommer", "Finn Eilertsen", "avstå", "$."]
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assert [t["head"] for t in tokens] == [1, 1, 0, -1]
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assert [t["dep"] for t in tokens] == ["appos", "nsubj", "ROOT", "punct"]
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ent_offsets = [
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(e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"]
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]
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biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O")
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assert biluo_tags == ["O", "U-PER", "O", "O"]
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def test_cli_converters_iob_to_docs():
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lines = [
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"I|O like|O London|I-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O",
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"I|O like|O London|B-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O",
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"I|PRP|O like|VBP|O London|NNP|I-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O",
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"I|PRP|O like|VBP|O London|NNP|B-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O",
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]
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input_data = "\n".join(lines)
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converted_docs = list(iob_to_docs(input_data, n_sents=10))
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assert len(converted_docs) == 1
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converted = docs_to_json(converted_docs)
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assert converted["id"] == 0
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assert len(converted["paragraphs"]) == 1
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assert len(converted["paragraphs"][0]["sentences"]) == 4
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for i in range(0, 4):
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sent = converted["paragraphs"][0]["sentences"][i]
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assert len(sent["tokens"]) == 8
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tokens = sent["tokens"]
|
|
expected = ["I", "like", "London", "and", "New", "York", "City", "."]
|
|
assert [t["orth"] for t in tokens] == expected
|
|
assert len(converted_docs[0].ents) == 8
|
|
for ent in converted_docs[0].ents:
|
|
assert ent.text in ["New York City", "London"]
|
|
|
|
|
|
def test_cli_converters_conll_ner_to_docs():
|
|
lines = [
|
|
"-DOCSTART- -X- O O",
|
|
"",
|
|
"I\tO",
|
|
"like\tO",
|
|
"London\tB-GPE",
|
|
"and\tO",
|
|
"New\tB-GPE",
|
|
"York\tI-GPE",
|
|
"City\tI-GPE",
|
|
".\tO",
|
|
"",
|
|
"I O",
|
|
"like O",
|
|
"London B-GPE",
|
|
"and O",
|
|
"New B-GPE",
|
|
"York I-GPE",
|
|
"City I-GPE",
|
|
". O",
|
|
"",
|
|
"I PRP O",
|
|
"like VBP O",
|
|
"London NNP B-GPE",
|
|
"and CC O",
|
|
"New NNP B-GPE",
|
|
"York NNP I-GPE",
|
|
"City NNP I-GPE",
|
|
". . O",
|
|
"",
|
|
"I PRP _ O",
|
|
"like VBP _ O",
|
|
"London NNP _ B-GPE",
|
|
"and CC _ O",
|
|
"New NNP _ B-GPE",
|
|
"York NNP _ I-GPE",
|
|
"City NNP _ I-GPE",
|
|
". . _ O",
|
|
"",
|
|
"I\tPRP\t_\tO",
|
|
"like\tVBP\t_\tO",
|
|
"London\tNNP\t_\tB-GPE",
|
|
"and\tCC\t_\tO",
|
|
"New\tNNP\t_\tB-GPE",
|
|
"York\tNNP\t_\tI-GPE",
|
|
"City\tNNP\t_\tI-GPE",
|
|
".\t.\t_\tO",
|
|
]
|
|
input_data = "\n".join(lines)
|
|
converted_docs = list(conll_ner_to_docs(input_data, n_sents=10))
|
|
assert len(converted_docs) == 1
|
|
converted = docs_to_json(converted_docs)
|
|
assert converted["id"] == 0
|
|
assert len(converted["paragraphs"]) == 1
|
|
assert len(converted["paragraphs"][0]["sentences"]) == 5
|
|
for i in range(0, 5):
|
|
sent = converted["paragraphs"][0]["sentences"][i]
|
|
assert len(sent["tokens"]) == 8
|
|
tokens = sent["tokens"]
|
|
# fmt: off
|
|
assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
|
|
# fmt: on
|
|
assert len(converted_docs[0].ents) == 10
|
|
for ent in converted_docs[0].ents:
|
|
assert ent.text in ["New York City", "London"]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"args,expected",
|
|
[
|
|
# fmt: off
|
|
(["--x.foo", "10"], {"x.foo": 10}),
|
|
(["--x.foo=10"], {"x.foo": 10}),
|
|
(["--x.foo", "bar"], {"x.foo": "bar"}),
|
|
(["--x.foo=bar"], {"x.foo": "bar"}),
|
|
(["--x.foo", "--x.bar", "baz"], {"x.foo": True, "x.bar": "baz"}),
|
|
(["--x.foo", "--x.bar=baz"], {"x.foo": True, "x.bar": "baz"}),
|
|
(["--x.foo", "10.1", "--x.bar", "--x.baz", "false"], {"x.foo": 10.1, "x.bar": True, "x.baz": False}),
|
|
(["--x.foo", "10.1", "--x.bar", "--x.baz=false"], {"x.foo": 10.1, "x.bar": True, "x.baz": False})
|
|
# fmt: on
|
|
],
|
|
)
|
|
def test_parse_config_overrides(args, expected):
|
|
assert parse_config_overrides(args) == expected
|
|
|
|
|
|
@pytest.mark.parametrize("args", [["--foo"], ["--x.foo", "bar", "--baz"]])
|
|
def test_parse_config_overrides_invalid(args):
|
|
with pytest.raises(NoSuchOption):
|
|
parse_config_overrides(args)
|
|
|
|
|
|
@pytest.mark.parametrize("args", [["--x.foo", "bar", "baz"], ["x.foo"]])
|
|
def test_parse_config_overrides_invalid_2(args):
|
|
with pytest.raises(SystemExit):
|
|
parse_config_overrides(args)
|
|
|
|
|
|
def test_parse_cli_overrides():
|
|
overrides = "--x.foo bar --x.bar=12 --x.baz false --y.foo=hello"
|
|
os.environ[ENV_VARS.CONFIG_OVERRIDES] = overrides
|
|
result = parse_config_overrides([])
|
|
assert len(result) == 4
|
|
assert result["x.foo"] == "bar"
|
|
assert result["x.bar"] == 12
|
|
assert result["x.baz"] is False
|
|
assert result["y.foo"] == "hello"
|
|
os.environ[ENV_VARS.CONFIG_OVERRIDES] = "--x"
|
|
assert parse_config_overrides([], env_var=None) == {}
|
|
with pytest.raises(SystemExit):
|
|
parse_config_overrides([])
|
|
os.environ[ENV_VARS.CONFIG_OVERRIDES] = "hello world"
|
|
with pytest.raises(SystemExit):
|
|
parse_config_overrides([])
|
|
del os.environ[ENV_VARS.CONFIG_OVERRIDES]
|
|
|
|
|
|
@pytest.mark.parametrize("lang", ["en", "nl"])
|
|
@pytest.mark.parametrize(
|
|
"pipeline",
|
|
[
|
|
["tagger", "parser", "ner"],
|
|
[],
|
|
["ner", "textcat", "sentencizer"],
|
|
["morphologizer", "spancat", "entity_linker"],
|
|
["spancat_singlelabel", "textcat_multilabel"],
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("optimize", ["efficiency", "accuracy"])
|
|
@pytest.mark.parametrize("pretraining", [True, False])
|
|
def test_init_config(lang, pipeline, optimize, pretraining):
|
|
# TODO: add more tests and also check for GPU with transformers
|
|
config = init_config(
|
|
lang=lang,
|
|
pipeline=pipeline,
|
|
optimize=optimize,
|
|
pretraining=pretraining,
|
|
gpu=False,
|
|
)
|
|
assert isinstance(config, Config)
|
|
if pretraining:
|
|
config["paths"]["raw_text"] = "my_data.jsonl"
|
|
load_model_from_config(config, auto_fill=True)
|
|
|
|
|
|
def test_model_recommendations():
|
|
for lang, data in RECOMMENDATIONS.items():
|
|
assert RecommendationSchema(**data)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"value",
|
|
[
|
|
# fmt: off
|
|
"parser,textcat,tagger",
|
|
" parser, textcat ,tagger ",
|
|
'parser,textcat,tagger',
|
|
' parser, textcat ,tagger ',
|
|
' "parser"," textcat " ,"tagger "',
|
|
" 'parser',' textcat ' ,'tagger '",
|
|
'[parser,textcat,tagger]',
|
|
'["parser","textcat","tagger"]',
|
|
'[" parser" ,"textcat ", " tagger " ]',
|
|
"[parser,textcat,tagger]",
|
|
"[ parser, textcat , tagger]",
|
|
"['parser','textcat','tagger']",
|
|
"[' parser' , 'textcat', ' tagger ' ]",
|
|
# fmt: on
|
|
],
|
|
)
|
|
def test_string_to_list(value):
|
|
assert string_to_list(value, intify=False) == ["parser", "textcat", "tagger"]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"value",
|
|
[
|
|
# fmt: off
|
|
"1,2,3",
|
|
'[1,2,3]',
|
|
'["1","2","3"]',
|
|
'[" 1" ,"2 ", " 3 " ]',
|
|
"[' 1' , '2', ' 3 ' ]",
|
|
# fmt: on
|
|
],
|
|
)
|
|
def test_string_to_list_intify(value):
|
|
assert string_to_list(value, intify=False) == ["1", "2", "3"]
|
|
assert string_to_list(value, intify=True) == [1, 2, 3]
|
|
|
|
|
|
def test_download_compatibility():
|
|
spec = SpecifierSet("==" + about.__version__)
|
|
spec.prereleases = False
|
|
if about.__version__ in spec:
|
|
model_name = "en_core_web_sm"
|
|
compatibility = get_compatibility()
|
|
version = get_version(model_name, compatibility)
|
|
assert get_minor_version(about.__version__) == get_minor_version(version)
|
|
|
|
|
|
def test_validate_compatibility_table():
|
|
spec = SpecifierSet("==" + about.__version__)
|
|
spec.prereleases = False
|
|
if about.__version__ in spec:
|
|
model_pkgs, compat = get_model_pkgs()
|
|
spacy_version = get_minor_version(about.__version__)
|
|
current_compat = compat.get(spacy_version, {})
|
|
assert len(current_compat) > 0
|
|
assert "en_core_web_sm" in current_compat
|
|
|
|
|
|
@pytest.mark.parametrize("component_name", ["ner", "textcat", "spancat", "tagger"])
|
|
def test_init_labels(component_name):
|
|
nlp = Dutch()
|
|
component = nlp.add_pipe(component_name)
|
|
for label in ["T1", "T2", "T3", "T4"]:
|
|
component.add_label(label)
|
|
assert len(nlp.get_pipe(component_name).labels) == 4
|
|
|
|
with make_tempdir() as tmp_dir:
|
|
_init_labels(nlp, tmp_dir)
|
|
|
|
config = init_config(
|
|
lang="nl",
|
|
pipeline=[component_name],
|
|
optimize="efficiency",
|
|
gpu=False,
|
|
)
|
|
config["initialize"]["components"][component_name] = {
|
|
"labels": {
|
|
"@readers": "spacy.read_labels.v1",
|
|
"path": f"{tmp_dir}/{component_name}.json",
|
|
}
|
|
}
|
|
|
|
nlp2 = load_model_from_config(config, auto_fill=True)
|
|
assert len(nlp2.get_pipe(component_name).labels) == 0
|
|
nlp2.initialize()
|
|
assert len(nlp2.get_pipe(component_name).labels) == 4
|
|
|
|
|
|
def test_get_third_party_dependencies():
|
|
# We can't easily test the detection of third-party packages here, but we
|
|
# can at least make sure that the function and its importlib magic runs.
|
|
nlp = Dutch()
|
|
# Test with component factory based on Cython module
|
|
nlp.add_pipe("tagger")
|
|
assert get_third_party_dependencies(nlp.config) == []
|
|
|
|
# Test with legacy function
|
|
nlp = Dutch()
|
|
nlp.add_pipe(
|
|
"textcat",
|
|
config={
|
|
"model": {
|
|
# Do not update from legacy architecture spacy.TextCatBOW.v1
|
|
"@architectures": "spacy.TextCatBOW.v1",
|
|
"exclusive_classes": True,
|
|
"ngram_size": 1,
|
|
"no_output_layer": False,
|
|
}
|
|
},
|
|
)
|
|
assert get_third_party_dependencies(nlp.config) == []
|
|
|
|
# Test with lang-specific factory
|
|
@Dutch.factory("third_party_test")
|
|
def test_factory(nlp, name):
|
|
return lambda x: x
|
|
|
|
nlp.add_pipe("third_party_test")
|
|
# Before #9674 this would throw an exception
|
|
get_third_party_dependencies(nlp.config)
|
|
|
|
|
|
@pytest.mark.slow
|
|
@pytest.mark.parametrize(
|
|
"factory_name,pipe_name",
|
|
[
|
|
("ner", "ner"),
|
|
("ner", "my_ner"),
|
|
("spancat", "spancat"),
|
|
("spancat", "my_spancat"),
|
|
],
|
|
)
|
|
def test_get_labels_from_model(factory_name, pipe_name):
|
|
labels = ("A", "B")
|
|
|
|
nlp = English()
|
|
pipe = nlp.add_pipe(factory_name, name=pipe_name)
|
|
for label in labels:
|
|
pipe.add_label(label)
|
|
nlp.initialize()
|
|
assert nlp.get_pipe(pipe_name).labels == labels
|
|
if factory_name == "spancat":
|
|
assert _get_labels_from_spancat(nlp)[pipe.key] == set(labels)
|
|
else:
|
|
assert _get_labels_from_model(nlp, factory_name) == set(labels)
|
|
|
|
|
|
def test_permitted_package_names():
|
|
# https://www.python.org/dev/peps/pep-0426/#name
|
|
assert _is_permitted_package_name("Meine_Bäume") == False
|
|
assert _is_permitted_package_name("_package") == False
|
|
assert _is_permitted_package_name("package_") == False
|
|
assert _is_permitted_package_name(".package") == False
|
|
assert _is_permitted_package_name("package.") == False
|
|
assert _is_permitted_package_name("-package") == False
|
|
assert _is_permitted_package_name("package-") == False
|
|
|
|
|
|
def test_debug_data_compile_gold():
|
|
nlp = English()
|
|
pred = Doc(nlp.vocab, words=["Token", ".", "New", "York", "City"])
|
|
ref = Doc(
|
|
nlp.vocab,
|
|
words=["Token", ".", "New York City"],
|
|
sent_starts=[True, False, True],
|
|
ents=["O", "O", "B-ENT"],
|
|
)
|
|
eg = Example(pred, ref)
|
|
data = _compile_gold([eg], ["ner"], nlp, True)
|
|
assert data["boundary_cross_ents"] == 0
|
|
|
|
pred = Doc(nlp.vocab, words=["Token", ".", "New", "York", "City"])
|
|
ref = Doc(
|
|
nlp.vocab,
|
|
words=["Token", ".", "New York City"],
|
|
sent_starts=[True, False, True],
|
|
ents=["O", "B-ENT", "I-ENT"],
|
|
)
|
|
eg = Example(pred, ref)
|
|
data = _compile_gold([eg], ["ner"], nlp, True)
|
|
assert data["boundary_cross_ents"] == 1
|
|
|
|
|
|
@pytest.mark.parametrize("component_name", ["spancat", "spancat_singlelabel"])
|
|
def test_debug_data_compile_gold_for_spans(component_name):
|
|
nlp = English()
|
|
spans_key = "sc"
|
|
|
|
pred = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
|
|
pred.spans[spans_key] = [Span(pred, 3, 6, "ORG"), Span(pred, 5, 6, "GPE")]
|
|
ref = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
|
|
ref.spans[spans_key] = [Span(ref, 3, 6, "ORG"), Span(ref, 5, 6, "GPE")]
|
|
eg = Example(pred, ref)
|
|
|
|
data = _compile_gold([eg], [component_name], nlp, True)
|
|
|
|
assert data["spancat"][spans_key] == Counter({"ORG": 1, "GPE": 1})
|
|
assert data["spans_length"][spans_key] == {"ORG": [3], "GPE": [1]}
|
|
assert data["spans_per_type"][spans_key] == {
|
|
"ORG": [Span(ref, 3, 6, "ORG")],
|
|
"GPE": [Span(ref, 5, 6, "GPE")],
|
|
}
|
|
assert data["sb_per_type"][spans_key] == {
|
|
"ORG": {"start": [ref[2:3]], "end": [ref[6:7]]},
|
|
"GPE": {"start": [ref[4:5]], "end": [ref[6:7]]},
|
|
}
|
|
|
|
|
|
def test_frequency_distribution_is_correct():
|
|
nlp = English()
|
|
docs = [
|
|
Doc(nlp.vocab, words=["Bank", "of", "China"]),
|
|
Doc(nlp.vocab, words=["China"]),
|
|
]
|
|
|
|
expected = Counter({"china": 0.5, "bank": 0.25, "of": 0.25})
|
|
freq_distribution = _get_distribution(docs, normalize=True)
|
|
assert freq_distribution == expected
|
|
|
|
|
|
def test_kl_divergence_computation_is_correct():
|
|
p = Counter({"a": 0.5, "b": 0.25})
|
|
q = Counter({"a": 0.25, "b": 0.50, "c": 0.15, "d": 0.10})
|
|
result = _get_kl_divergence(p, q)
|
|
expected = 0.1733
|
|
assert math.isclose(result, expected, rel_tol=1e-3)
|
|
|
|
|
|
def test_get_span_characteristics_return_value():
|
|
nlp = English()
|
|
spans_key = "sc"
|
|
|
|
pred = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
|
|
pred.spans[spans_key] = [Span(pred, 3, 6, "ORG"), Span(pred, 5, 6, "GPE")]
|
|
ref = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
|
|
ref.spans[spans_key] = [Span(ref, 3, 6, "ORG"), Span(ref, 5, 6, "GPE")]
|
|
eg = Example(pred, ref)
|
|
|
|
examples = [eg]
|
|
data = _compile_gold(examples, ["spancat"], nlp, True)
|
|
span_characteristics = _get_span_characteristics(
|
|
examples=examples, compiled_gold=data, spans_key=spans_key
|
|
)
|
|
|
|
assert {"sd", "bd", "lengths"}.issubset(span_characteristics.keys())
|
|
assert span_characteristics["min_length"] == 1
|
|
assert span_characteristics["max_length"] == 3
|
|
|
|
|
|
def test_ensure_print_span_characteristics_wont_fail():
|
|
"""Test if interface between two methods aren't destroyed if refactored"""
|
|
nlp = English()
|
|
spans_key = "sc"
|
|
|
|
pred = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
|
|
pred.spans[spans_key] = [Span(pred, 3, 6, "ORG"), Span(pred, 5, 6, "GPE")]
|
|
ref = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."])
|
|
ref.spans[spans_key] = [Span(ref, 3, 6, "ORG"), Span(ref, 5, 6, "GPE")]
|
|
eg = Example(pred, ref)
|
|
|
|
examples = [eg]
|
|
data = _compile_gold(examples, ["spancat"], nlp, True)
|
|
span_characteristics = _get_span_characteristics(
|
|
examples=examples, compiled_gold=data, spans_key=spans_key
|
|
)
|
|
_print_span_characteristics(span_characteristics)
|
|
|
|
|
|
@pytest.mark.parametrize("threshold", [70, 80, 85, 90, 95])
|
|
def test_span_length_freq_dist_threshold_must_be_correct(threshold):
|
|
sample_span_lengths = {
|
|
"span_type_1": [1, 4, 4, 5],
|
|
"span_type_2": [5, 3, 3, 2],
|
|
"span_type_3": [3, 1, 3, 3],
|
|
}
|
|
span_freqs = _get_spans_length_freq_dist(sample_span_lengths, threshold)
|
|
assert sum(span_freqs.values()) >= threshold
|
|
|
|
|
|
def test_span_length_freq_dist_output_must_be_correct():
|
|
sample_span_lengths = {
|
|
"span_type_1": [1, 4, 4, 5],
|
|
"span_type_2": [5, 3, 3, 2],
|
|
"span_type_3": [3, 1, 3, 3],
|
|
}
|
|
threshold = 90
|
|
span_freqs = _get_spans_length_freq_dist(sample_span_lengths, threshold)
|
|
assert sum(span_freqs.values()) >= threshold
|
|
assert list(span_freqs.keys()) == [3, 1, 4, 5, 2]
|
|
|
|
|
|
def test_applycli_empty_dir():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "test.spacy"
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
|
|
|
|
def test_applycli_docbin():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
nlp = spacy.blank("en")
|
|
doc = nlp("testing apply cli.")
|
|
# test empty DocBin case
|
|
docbin = DocBin()
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
docbin.add(doc)
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
|
|
|
|
def test_applycli_jsonl():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
data = [{"field": "Testing apply cli.", "key": 234}]
|
|
data2 = [{"field": "234"}]
|
|
srsly.write_jsonl(data_path / "test.jsonl", data)
|
|
apply(data_path, output, "blank:en", "field", 1, 1)
|
|
srsly.write_jsonl(data_path / "test2.jsonl", data2)
|
|
apply(data_path, output, "blank:en", "field", 1, 1)
|
|
|
|
|
|
def test_applycli_txt():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
with open(data_path / "test.foo", "w") as ftest:
|
|
ftest.write("Testing apply cli.")
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
|
|
|
|
def test_applycli_mixed():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
text = "Testing apply cli"
|
|
nlp = spacy.blank("en")
|
|
doc = nlp(text)
|
|
jsonl_data = [{"text": text}]
|
|
srsly.write_jsonl(data_path / "test.jsonl", jsonl_data)
|
|
docbin = DocBin()
|
|
docbin.add(doc)
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
with open(data_path / "test.txt", "w") as ftest:
|
|
ftest.write(text)
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
# Check whether it worked
|
|
result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
|
|
assert len(result) == 3
|
|
for doc in result:
|
|
assert doc.text == text
|
|
|
|
|
|
def test_applycli_user_data():
|
|
Doc.set_extension("ext", default=0)
|
|
val = ("ext", 0)
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
nlp = spacy.blank("en")
|
|
doc = nlp("testing apply cli.")
|
|
doc._.ext = val
|
|
docbin = DocBin(store_user_data=True)
|
|
docbin.add(doc)
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
apply(data_path, output, "blank:en", "", 1, 1)
|
|
result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
|
|
assert result[0]._.ext == val
|
|
|
|
|
|
def test_cli_find_threshold(capsys):
|
|
def make_examples(nlp: Language) -> List[Example]:
|
|
docs: List[Example] = []
|
|
|
|
for t in [
|
|
(
|
|
"I am angry and confused in the Bank of America.",
|
|
{
|
|
"cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0},
|
|
"spans": {"sc": [(31, 46, "ORG")]},
|
|
},
|
|
),
|
|
(
|
|
"I am confused but happy in New York.",
|
|
{
|
|
"cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0},
|
|
"spans": {"sc": [(27, 35, "GPE")]},
|
|
},
|
|
),
|
|
]:
|
|
doc = nlp.make_doc(t[0])
|
|
docs.append(Example.from_dict(doc, t[1]))
|
|
|
|
return docs
|
|
|
|
def init_nlp(
|
|
components: Tuple[Tuple[str, Dict[str, Any]], ...] = ()
|
|
) -> Tuple[Language, List[Example]]:
|
|
new_nlp = English()
|
|
new_nlp.add_pipe( # type: ignore
|
|
factory_name="textcat_multilabel",
|
|
name="tc_multi",
|
|
config={"threshold": 0.9},
|
|
)
|
|
|
|
# Append additional components to pipeline.
|
|
for cfn, comp_config in components:
|
|
new_nlp.add_pipe(cfn, config=comp_config)
|
|
|
|
new_examples = make_examples(new_nlp)
|
|
new_nlp.initialize(get_examples=lambda: new_examples)
|
|
for i in range(5):
|
|
new_nlp.update(new_examples)
|
|
|
|
return new_nlp, new_examples
|
|
|
|
with make_tempdir() as docs_dir:
|
|
# Check whether find_threshold() identifies lowest threshold above 0 as (first) ideal threshold, as this matches
|
|
# the current model behavior with the examples above. This can break once the model behavior changes and serves
|
|
# mostly as a smoke test.
|
|
nlp, examples = init_nlp()
|
|
DocBin(docs=[example.reference for example in examples]).to_disk(
|
|
docs_dir / "docs.spacy"
|
|
)
|
|
with make_tempdir() as nlp_dir:
|
|
nlp.to_disk(nlp_dir)
|
|
best_threshold, best_score, res = find_threshold(
|
|
model=nlp_dir,
|
|
data_path=docs_dir / "docs.spacy",
|
|
pipe_name="tc_multi",
|
|
threshold_key="threshold",
|
|
scores_key="cats_macro_f",
|
|
silent=True,
|
|
)
|
|
assert best_score == max(res.values())
|
|
assert res[1.0] == 0.0
|
|
|
|
# Test with spancat.
|
|
nlp, _ = init_nlp((("spancat", {}),))
|
|
with make_tempdir() as nlp_dir:
|
|
nlp.to_disk(nlp_dir)
|
|
best_threshold, best_score, res = find_threshold(
|
|
model=nlp_dir,
|
|
data_path=docs_dir / "docs.spacy",
|
|
pipe_name="spancat",
|
|
threshold_key="threshold",
|
|
scores_key="spans_sc_f",
|
|
silent=True,
|
|
)
|
|
assert best_score == max(res.values())
|
|
assert res[1.0] == 0.0
|
|
|
|
# Having multiple textcat_multilabel components should work, since the name has to be specified.
|
|
nlp, _ = init_nlp((("textcat_multilabel", {}),))
|
|
with make_tempdir() as nlp_dir:
|
|
nlp.to_disk(nlp_dir)
|
|
assert find_threshold(
|
|
model=nlp_dir,
|
|
data_path=docs_dir / "docs.spacy",
|
|
pipe_name="tc_multi",
|
|
threshold_key="threshold",
|
|
scores_key="cats_macro_f",
|
|
silent=True,
|
|
)
|
|
|
|
# Specifying the name of an non-existing pipe should fail.
|
|
nlp, _ = init_nlp()
|
|
with make_tempdir() as nlp_dir:
|
|
nlp.to_disk(nlp_dir)
|
|
with pytest.raises(AttributeError):
|
|
find_threshold(
|
|
model=nlp_dir,
|
|
data_path=docs_dir / "docs.spacy",
|
|
pipe_name="_",
|
|
threshold_key="threshold",
|
|
scores_key="cats_macro_f",
|
|
silent=True,
|
|
)
|
|
|
|
|
|
def test_walk_directory():
|
|
with make_tempdir() as d:
|
|
files = [
|
|
"data1.iob",
|
|
"data2.iob",
|
|
"data3.json",
|
|
"data4.conll",
|
|
"data5.conll",
|
|
"data6.conll",
|
|
"data7.txt",
|
|
]
|
|
|
|
for f in files:
|
|
Path(d / f).touch()
|
|
|
|
assert (len(walk_directory(d))) == 7
|
|
assert (len(walk_directory(d, suffix=None))) == 7
|
|
assert (len(walk_directory(d, suffix="json"))) == 1
|
|
assert (len(walk_directory(d, suffix="iob"))) == 2
|
|
assert (len(walk_directory(d, suffix="conll"))) == 3
|
|
assert (len(walk_directory(d, suffix="pdf"))) == 0
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_basic():
|
|
examples = [
|
|
("She likes green eggs", {"lemmas": ["she", "like", "green", "egg"]}),
|
|
("Eat blue ham", {"lemmas": ["eat", "blue", "ham"]}),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
# ref test_edit_tree_lemmatizer::test_initialize_from_labels
|
|
# this results in 4 trees
|
|
assert len(data["lemmatizer_trees"]) == 4
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_partial():
|
|
partial_examples = [
|
|
# partial annotation
|
|
("She likes green eggs", {"lemmas": ["", "like", "green", ""]}),
|
|
# misaligned partial annotation
|
|
(
|
|
"He hates green eggs",
|
|
{
|
|
"words": ["He", "hat", "es", "green", "eggs"],
|
|
"lemmas": ["", "hat", "e", "green", ""],
|
|
},
|
|
),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in partial_examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
assert data["partial_lemma_annotations"] == 2
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_low_cardinality():
|
|
low_cardinality_examples = [
|
|
("She likes green eggs", {"lemmas": ["no", "no", "no", "no"]}),
|
|
("Eat blue ham", {"lemmas": ["no", "no", "no"]}),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in low_cardinality_examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
assert data["n_low_cardinality_lemmas"] == 2
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_not_annotated():
|
|
unannotated_examples = [
|
|
("She likes green eggs", {}),
|
|
("Eat blue ham", {}),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in unannotated_examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
assert data["no_lemma_annotations"] == 2
|