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""" |
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Tests for setting request properties of servers |
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""" |
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import json |
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import pytest |
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import stanza.server as corenlp |
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from stanza.protobuf import Document |
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from stanza.tests import TEST_WORKING_DIR, compare_ignoring_whitespace |
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pytestmark = pytest.mark.client |
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EN_DOC = "Joe Smith lives in California." |
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EN_DOC_GOLD = """ |
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Sentence #1 (6 tokens): |
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Joe Smith lives in California. |
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Tokens: |
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[Text=Joe CharacterOffsetBegin=0 CharacterOffsetEnd=3 PartOfSpeech=NNP] |
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[Text=Smith CharacterOffsetBegin=4 CharacterOffsetEnd=9 PartOfSpeech=NNP] |
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[Text=lives CharacterOffsetBegin=10 CharacterOffsetEnd=15 PartOfSpeech=VBZ] |
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[Text=in CharacterOffsetBegin=16 CharacterOffsetEnd=18 PartOfSpeech=IN] |
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[Text=California CharacterOffsetBegin=19 CharacterOffsetEnd=29 PartOfSpeech=NNP] |
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[Text=. CharacterOffsetBegin=29 CharacterOffsetEnd=30 PartOfSpeech=.] |
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""" |
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GERMAN_DOC = "Angela Merkel ist seit 2005 Bundeskanzlerin der Bundesrepublik Deutschland." |
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GERMAN_DOC_GOLD = """ |
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Sentence #1 (10 tokens): |
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Angela Merkel ist seit 2005 Bundeskanzlerin der Bundesrepublik Deutschland. |
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Tokens: |
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[Text=Angela CharacterOffsetBegin=0 CharacterOffsetEnd=6 PartOfSpeech=PROPN] |
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[Text=Merkel CharacterOffsetBegin=7 CharacterOffsetEnd=13 PartOfSpeech=PROPN] |
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[Text=ist CharacterOffsetBegin=14 CharacterOffsetEnd=17 PartOfSpeech=AUX] |
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[Text=seit CharacterOffsetBegin=18 CharacterOffsetEnd=22 PartOfSpeech=ADP] |
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[Text=2005 CharacterOffsetBegin=23 CharacterOffsetEnd=27 PartOfSpeech=NUM] |
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[Text=Bundeskanzlerin CharacterOffsetBegin=28 CharacterOffsetEnd=43 PartOfSpeech=NOUN] |
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[Text=der CharacterOffsetBegin=44 CharacterOffsetEnd=47 PartOfSpeech=DET] |
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[Text=Bundesrepublik CharacterOffsetBegin=48 CharacterOffsetEnd=62 PartOfSpeech=PROPN] |
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[Text=Deutschland CharacterOffsetBegin=63 CharacterOffsetEnd=74 PartOfSpeech=PROPN] |
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[Text=. CharacterOffsetBegin=74 CharacterOffsetEnd=75 PartOfSpeech=PUNCT] |
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""" |
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FRENCH_CUSTOM_PROPS = {'annotators': 'tokenize,ssplit,mwt,pos,parse', |
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'tokenize.language': 'fr', |
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'pos.model': 'edu/stanford/nlp/models/pos-tagger/french-ud.tagger', |
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'parse.model': 'edu/stanford/nlp/models/srparser/frenchSR.ser.gz', |
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'mwt.mappingFile': 'edu/stanford/nlp/models/mwt/french/french-mwt.tsv', |
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'mwt.pos.model': 'edu/stanford/nlp/models/mwt/french/french-mwt.tagger', |
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'mwt.statisticalMappingFile': 'edu/stanford/nlp/models/mwt/french/french-mwt-statistical.tsv', |
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'mwt.preserveCasing': 'false', |
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'outputFormat': 'text'} |
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FRENCH_EXTRA_PROPS = {'annotators': 'tokenize,ssplit,mwt,pos,depparse', |
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'tokenize.language': 'fr', |
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'pos.model': 'edu/stanford/nlp/models/pos-tagger/french-ud.tagger', |
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'mwt.mappingFile': 'edu/stanford/nlp/models/mwt/french/french-mwt.tsv', |
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'mwt.pos.model': 'edu/stanford/nlp/models/mwt/french/french-mwt.tagger', |
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'mwt.statisticalMappingFile': 'edu/stanford/nlp/models/mwt/french/french-mwt-statistical.tsv', |
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'mwt.preserveCasing': 'false', |
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'depparse.model': 'edu/stanford/nlp/models/parser/nndep/UD_French.gz'} |
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FRENCH_DOC = "Cette enquête préliminaire fait suite aux révélations de l’hebdomadaire quelques jours plus tôt." |
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FRENCH_CUSTOM_GOLD = """ |
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Sentence #1 (16 tokens): |
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Cette enquête préliminaire fait suite aux révélations de l’hebdomadaire quelques jours plus tôt. |
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Tokens: |
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[Text=Cette CharacterOffsetBegin=0 CharacterOffsetEnd=5 PartOfSpeech=DET] |
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[Text=enquête CharacterOffsetBegin=6 CharacterOffsetEnd=13 PartOfSpeech=NOUN] |
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[Text=préliminaire CharacterOffsetBegin=14 CharacterOffsetEnd=26 PartOfSpeech=ADJ] |
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[Text=fait CharacterOffsetBegin=27 CharacterOffsetEnd=31 PartOfSpeech=VERB] |
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[Text=suite CharacterOffsetBegin=32 CharacterOffsetEnd=37 PartOfSpeech=NOUN] |
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[Text=à CharacterOffsetBegin=38 CharacterOffsetEnd=41 PartOfSpeech=ADP] |
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[Text=les CharacterOffsetBegin=38 CharacterOffsetEnd=41 PartOfSpeech=DET] |
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[Text=révélations CharacterOffsetBegin=42 CharacterOffsetEnd=53 PartOfSpeech=NOUN] |
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[Text=de CharacterOffsetBegin=54 CharacterOffsetEnd=56 PartOfSpeech=ADP] |
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[Text=l’ CharacterOffsetBegin=57 CharacterOffsetEnd=59 PartOfSpeech=NOUN] |
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[Text=hebdomadaire CharacterOffsetBegin=59 CharacterOffsetEnd=71 PartOfSpeech=ADJ] |
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[Text=quelques CharacterOffsetBegin=72 CharacterOffsetEnd=80 PartOfSpeech=DET] |
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[Text=jours CharacterOffsetBegin=81 CharacterOffsetEnd=86 PartOfSpeech=NOUN] |
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[Text=plus CharacterOffsetBegin=87 CharacterOffsetEnd=91 PartOfSpeech=ADV] |
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[Text=tôt CharacterOffsetBegin=92 CharacterOffsetEnd=95 PartOfSpeech=ADV] |
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[Text=. CharacterOffsetBegin=95 CharacterOffsetEnd=96 PartOfSpeech=PUNCT] |
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Constituency parse: |
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(ROOT |
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(SENT |
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(NP (DET Cette) |
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(MWN (NOUN enquête) (ADJ préliminaire))) |
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(VN |
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(MWV (VERB fait) (NOUN suite))) |
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(PP (ADP à) |
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(NP (DET les) (NOUN révélations) |
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(PP (ADP de) |
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(NP (NOUN l’) |
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(AP (ADJ hebdomadaire)))))) |
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(NP (DET quelques) (NOUN jours)) |
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(AdP (ADV plus) (ADV tôt)) |
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(PUNCT .))) |
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""" |
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FRENCH_EXTRA_GOLD = """ |
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Sentence #1 (16 tokens): |
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Cette enquête préliminaire fait suite aux révélations de l’hebdomadaire quelques jours plus tôt. |
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Tokens: |
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[Text=Cette CharacterOffsetBegin=0 CharacterOffsetEnd=5 PartOfSpeech=DET] |
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[Text=enquête CharacterOffsetBegin=6 CharacterOffsetEnd=13 PartOfSpeech=NOUN] |
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[Text=préliminaire CharacterOffsetBegin=14 CharacterOffsetEnd=26 PartOfSpeech=ADJ] |
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[Text=fait CharacterOffsetBegin=27 CharacterOffsetEnd=31 PartOfSpeech=VERB] |
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[Text=suite CharacterOffsetBegin=32 CharacterOffsetEnd=37 PartOfSpeech=NOUN] |
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[Text=à CharacterOffsetBegin=38 CharacterOffsetEnd=41 PartOfSpeech=ADP] |
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[Text=les CharacterOffsetBegin=38 CharacterOffsetEnd=41 PartOfSpeech=DET] |
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[Text=révélations CharacterOffsetBegin=42 CharacterOffsetEnd=53 PartOfSpeech=NOUN] |
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[Text=de CharacterOffsetBegin=54 CharacterOffsetEnd=56 PartOfSpeech=ADP] |
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[Text=l’ CharacterOffsetBegin=57 CharacterOffsetEnd=59 PartOfSpeech=NOUN] |
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[Text=hebdomadaire CharacterOffsetBegin=59 CharacterOffsetEnd=71 PartOfSpeech=ADJ] |
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[Text=quelques CharacterOffsetBegin=72 CharacterOffsetEnd=80 PartOfSpeech=DET] |
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[Text=jours CharacterOffsetBegin=81 CharacterOffsetEnd=86 PartOfSpeech=NOUN] |
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[Text=plus CharacterOffsetBegin=87 CharacterOffsetEnd=91 PartOfSpeech=ADV] |
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[Text=tôt CharacterOffsetBegin=92 CharacterOffsetEnd=95 PartOfSpeech=ADV] |
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[Text=. CharacterOffsetBegin=95 CharacterOffsetEnd=96 PartOfSpeech=PUNCT] |
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Dependency Parse (enhanced plus plus dependencies): |
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root(ROOT-0, fait-4) |
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det(enquête-2, Cette-1) |
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nsubj(fait-4, enquête-2) |
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amod(enquête-2, préliminaire-3) |
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obj(fait-4, suite-5) |
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case(révélations-8, à-6) |
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det(révélations-8, les-7) |
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obl:à(fait-4, révélations-8) |
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case(l’-10, de-9) |
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nmod:de(révélations-8, l’-10) |
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amod(révélations-8, hebdomadaire-11) |
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det(jours-13, quelques-12) |
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obl(fait-4, jours-13) |
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advmod(tôt-15, plus-14) |
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advmod(jours-13, tôt-15) |
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punct(fait-4, .-16) |
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""" |
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FRENCH_JSON_GOLD = json.loads(open(f'{TEST_WORKING_DIR}/out/example_french.json', encoding="utf-8").read()) |
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ES_DOC = 'Andrés Manuel López Obrador es el presidente de México.' |
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ES_PROPS = {'annotators': 'tokenize,ssplit,mwt,pos,depparse', 'tokenize.language': 'es', |
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'pos.model': 'edu/stanford/nlp/models/pos-tagger/spanish-ud.tagger', |
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'mwt.mappingFile': 'edu/stanford/nlp/models/mwt/spanish/spanish-mwt.tsv', |
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'depparse.model': 'edu/stanford/nlp/models/parser/nndep/UD_Spanish.gz'} |
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ES_PROPS_GOLD = """ |
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Sentence #1 (10 tokens): |
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Andrés Manuel López Obrador es el presidente de México. |
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Tokens: |
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[Text=Andrés CharacterOffsetBegin=0 CharacterOffsetEnd=6 PartOfSpeech=PROPN] |
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[Text=Manuel CharacterOffsetBegin=7 CharacterOffsetEnd=13 PartOfSpeech=PROPN] |
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[Text=López CharacterOffsetBegin=14 CharacterOffsetEnd=19 PartOfSpeech=PROPN] |
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[Text=Obrador CharacterOffsetBegin=20 CharacterOffsetEnd=27 PartOfSpeech=PROPN] |
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[Text=es CharacterOffsetBegin=28 CharacterOffsetEnd=30 PartOfSpeech=AUX] |
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[Text=el CharacterOffsetBegin=31 CharacterOffsetEnd=33 PartOfSpeech=DET] |
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[Text=presidente CharacterOffsetBegin=34 CharacterOffsetEnd=44 PartOfSpeech=NOUN] |
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[Text=de CharacterOffsetBegin=45 CharacterOffsetEnd=47 PartOfSpeech=ADP] |
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[Text=México CharacterOffsetBegin=48 CharacterOffsetEnd=54 PartOfSpeech=PROPN] |
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[Text=. CharacterOffsetBegin=54 CharacterOffsetEnd=55 PartOfSpeech=PUNCT] |
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Dependency Parse (enhanced plus plus dependencies): |
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root(ROOT-0, presidente-7) |
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nsubj(presidente-7, Andrés-1) |
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flat(Andrés-1, Manuel-2) |
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flat(Andrés-1, López-3) |
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flat(Andrés-1, Obrador-4) |
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cop(presidente-7, es-5) |
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det(presidente-7, el-6) |
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case(México-9, de-8) |
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nmod:de(presidente-7, México-9) |
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punct(presidente-7, .-10) |
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""" |
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class TestServerRequest: |
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@pytest.fixture(scope="class") |
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def corenlp_client(self): |
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""" Client to run tests on """ |
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client = corenlp.CoreNLPClient(annotators='tokenize,ssplit,pos', server_id='stanza_request_tests_server') |
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yield client |
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client.stop() |
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def test_basic(self, corenlp_client): |
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""" Basic test of making a request, test default output format is a Document """ |
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ann = corenlp_client.annotate(EN_DOC, output_format="text") |
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compare_ignoring_whitespace(ann, EN_DOC_GOLD) |
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ann = corenlp_client.annotate(EN_DOC) |
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assert isinstance(ann, Document) |
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def test_python_dict(self, corenlp_client): |
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""" Test using a Python dictionary to specify all request properties """ |
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ann = corenlp_client.annotate(ES_DOC, properties=ES_PROPS, output_format="text") |
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compare_ignoring_whitespace(ann, ES_PROPS_GOLD) |
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ann = corenlp_client.annotate(FRENCH_DOC, properties=FRENCH_CUSTOM_PROPS) |
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compare_ignoring_whitespace(ann, FRENCH_CUSTOM_GOLD) |
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def test_lang_setting(self, corenlp_client): |
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""" Test using a Stanford CoreNLP supported languages as a properties key """ |
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ann = corenlp_client.annotate(GERMAN_DOC, properties="german", output_format="text") |
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compare_ignoring_whitespace(ann, GERMAN_DOC_GOLD) |
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def test_annotators_and_output_format(self, corenlp_client): |
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""" Test setting the annotators and output_format """ |
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ann = corenlp_client.annotate(FRENCH_DOC, properties=FRENCH_EXTRA_PROPS, |
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annotators="tokenize,ssplit,mwt,pos", output_format="json") |
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assert ann == FRENCH_JSON_GOLD |
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