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215
spacy/tests/training/test_rehearse.py
View File

@ -7,139 +7,182 @@ from spacy.training import Example
TRAIN_DATA = [ TRAIN_DATA = [
( (
'Who is Kofi Annan?', "Who is Kofi Annan?",
{ {
'entities': [(7, 18, 'PERSON')], "entities": [(7, 18, "PERSON")],
'tags': ['PRON', 'AUX', 'PROPN', 'PRON', 'PUNCT'], "tags": ["PRON", "AUX", "PROPN", "PRON", "PUNCT"],
'heads': [1, 1, 3, 1, 1], "heads": [1, 1, 3, 1, 1],
'deps': ['attr', 'ROOT', 'compound', 'nsubj', 'punct'], "deps": ["attr", "ROOT", "compound", "nsubj", "punct"],
'morphs': ['', 'Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin', 'Number=Sing', 'Number=Sing', 'PunctType=Peri'], "morphs": [
'cats': {'question': 1.0} "",
} "Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
), "Number=Sing",
( "Number=Sing",
'Who is Steve Jobs?', "PunctType=Peri",
{ ],
'entities': [(7, 17, 'PERSON')], "cats": {"question": 1.0},
'tags': ['PRON', 'AUX', 'PROPN', 'PRON', 'PUNCT'],
'heads': [1, 1, 3, 1, 1],
'deps': ['attr', 'ROOT', 'compound', 'nsubj', 'punct'],
'morphs': ['', 'Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin', 'Number=Sing', 'Number=Sing', 'PunctType=Peri'],
'cats': {'question': 1.0}
}
),
(
'Bob is a nice person.',
{
'entities': [(0, 3, 'PERSON')],
'tags': ['PROPN', 'AUX', 'DET', 'ADJ', 'NOUN', 'PUNCT'],
'heads': [1, 1, 4, 4, 1, 1],
'deps': ['nsubj', 'ROOT', 'det', 'amod', 'attr', 'punct'],
'morphs': ['Number=Sing', 'Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin', 'Definite=Ind|PronType=Art', 'Degree=Pos', 'Number=Sing', 'PunctType=Peri'],
'cats': {'statement': 1.0}
}, },
), ),
( (
'Hi Anil, how are you?', "Who is Steve Jobs?",
{ {
'entities': [(3, 7, 'PERSON')], "entities": [(7, 17, "PERSON")],
'tags': ['INTJ', 'PROPN', 'PUNCT', 'ADV', 'AUX', 'PRON', 'PUNCT'], "tags": ["PRON", "AUX", "PROPN", "PRON", "PUNCT"],
'deps': ['intj', 'npadvmod', 'punct', 'advmod', 'ROOT', 'nsubj', 'punct'], "heads": [1, 1, 3, 1, 1],
'heads': [4, 0, 4, 4, 4, 4, 4], "deps": ["attr", "ROOT", "compound", "nsubj", "punct"],
'morphs': ['', 'Number=Sing', 'PunctType=Comm', '', 'Mood=Ind|Tense=Pres|VerbForm=Fin', 'Case=Nom|Person=2|PronType=Prs', 'PunctType=Peri'], "morphs": [
'cats': {'greeting': 1.0, 'question': 1.0} "",
} "Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
"Number=Sing",
"Number=Sing",
"PunctType=Peri",
],
"cats": {"question": 1.0},
},
), ),
( (
'I like London and Berlin.', "Bob is a nice person.",
{ {
'entities': [(7, 13, 'LOC'), (18, 24, 'LOC')], "entities": [(0, 3, "PERSON")],
'tags': ['PROPN', 'VERB', 'PROPN', 'CCONJ', 'PROPN', 'PUNCT'], "tags": ["PROPN", "AUX", "DET", "ADJ", "NOUN", "PUNCT"],
'deps': ['nsubj', 'ROOT', 'dobj', 'cc', 'conj', 'punct'], "heads": [1, 1, 4, 4, 1, 1],
'heads': [1, 1, 1, 2, 2, 1], "deps": ["nsubj", "ROOT", "det", "amod", "attr", "punct"],
'morphs': ['Case=Nom|Number=Sing|Person=1|PronType=Prs', 'Tense=Pres|VerbForm=Fin', 'Number=Sing', 'ConjType=Cmp', 'Number=Sing', 'PunctType=Peri'], "morphs": [
'cats': {'statement': 1.0} "Number=Sing",
} "Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
) "Definite=Ind|PronType=Art",
"Degree=Pos",
"Number=Sing",
"PunctType=Peri",
],
"cats": {"statement": 1.0},
},
),
(
"Hi Anil, how are you?",
{
"entities": [(3, 7, "PERSON")],
"tags": ["INTJ", "PROPN", "PUNCT", "ADV", "AUX", "PRON", "PUNCT"],
"deps": ["intj", "npadvmod", "punct", "advmod", "ROOT", "nsubj", "punct"],
"heads": [4, 0, 4, 4, 4, 4, 4],
"morphs": [
"",
"Number=Sing",
"PunctType=Comm",
"",
"Mood=Ind|Tense=Pres|VerbForm=Fin",
"Case=Nom|Person=2|PronType=Prs",
"PunctType=Peri",
],
"cats": {"greeting": 1.0, "question": 1.0},
},
),
(
"I like London and Berlin.",
{
"entities": [(7, 13, "LOC"), (18, 24, "LOC")],
"tags": ["PROPN", "VERB", "PROPN", "CCONJ", "PROPN", "PUNCT"],
"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
"heads": [1, 1, 1, 2, 2, 1],
"morphs": [
"Case=Nom|Number=Sing|Person=1|PronType=Prs",
"Tense=Pres|VerbForm=Fin",
"Number=Sing",
"ConjType=Cmp",
"Number=Sing",
"PunctType=Peri",
],
"cats": {"statement": 1.0},
},
),
] ]
REHEARSE_DATA = [ REHEARSE_DATA = [
( (
'Hi Anil', "Hi Anil",
{ {
'entities': [(3, 7, 'PERSON')], "entities": [(3, 7, "PERSON")],
'tags': ['INTJ', 'PROPN'], "tags": ["INTJ", "PROPN"],
'deps': ['ROOT', 'npadvmod'], "deps": ["ROOT", "npadvmod"],
'heads': [0, 0], "heads": [0, 0],
'morphs': ['', 'Number=Sing'], "morphs": ["", "Number=Sing"],
'cats': {'greeting': 1.0} "cats": {"greeting": 1.0},
} },
), ),
( (
'Hi Ravish, how you doing?', "Hi Ravish, how you doing?",
{ {
'entities': [(3, 9, 'PERSON')], "entities": [(3, 9, "PERSON")],
'tags': ['INTJ', 'PROPN', 'PUNCT', 'ADV', 'AUX', 'PRON', 'PUNCT'], "tags": ["INTJ", "PROPN", "PUNCT", "ADV", "AUX", "PRON", "PUNCT"],
'deps': ['intj', 'ROOT', 'punct', 'advmod', 'nsubj', 'advcl', 'punct'], "deps": ["intj", "ROOT", "punct", "advmod", "nsubj", "advcl", "punct"],
'heads': [1, 1, 1, 5, 5, 1, 1], "heads": [1, 1, 1, 5, 5, 1, 1],
'morphs': ['', 'VerbForm=Inf', 'PunctType=Comm', '', 'Case=Nom|Person=2|PronType=Prs', 'Aspect=Prog|Tense=Pres|VerbForm=Part', 'PunctType=Peri'], "morphs": [
'cats': {'greeting': 1.0, 'question': 1.0} "",
} "VerbForm=Inf",
"PunctType=Comm",
"",
"Case=Nom|Person=2|PronType=Prs",
"Aspect=Prog|Tense=Pres|VerbForm=Part",
"PunctType=Peri",
],
"cats": {"greeting": 1.0, "question": 1.0},
},
), ),
# UTENSIL new label # UTENSIL new label
( (
'Natasha bought new forks.', "Natasha bought new forks.",
{ {
'entities': [(0, 7, 'PERSON'), (19, 24, 'UTENSIL')], "entities": [(0, 7, "PERSON"), (19, 24, "UTENSIL")],
'tags': ['PROPN', 'VERB', 'ADJ', 'NOUN', 'PUNCT'], "tags": ["PROPN", "VERB", "ADJ", "NOUN", "PUNCT"],
'deps': ['nsubj', 'ROOT', 'amod', 'dobj', 'punct'], "deps": ["nsubj", "ROOT", "amod", "dobj", "punct"],
'heads': [1, 1, 3, 1, 1], "heads": [1, 1, 3, 1, 1],
'morphs': ['Number=Sing', 'Tense=Past|VerbForm=Fin', 'Degree=Pos', 'Number=Plur', 'PunctType=Peri'], "morphs": [
'cats': {'statement': 1.0} "Number=Sing",
} "Tense=Past|VerbForm=Fin",
) "Degree=Pos",
"Number=Plur",
"PunctType=Peri",
],
"cats": {"statement": 1.0},
},
),
] ]
def _add_ner_label(ner, data): def _add_ner_label(ner, data):
for _, annotations in data: for _, annotations in data:
for ent in annotations['entities']: for ent in annotations["entities"]:
ner.add_label(ent[2]) ner.add_label(ent[2])
def _add_tagger_label(tagger, data): def _add_tagger_label(tagger, data):
for _, annotations in data: for _, annotations in data:
for tag in annotations['tags']: for tag in annotations["tags"]:
tagger.add_label(tag) tagger.add_label(tag)
def _add_parser_label(parser, data): def _add_parser_label(parser, data):
for _, annotations in data: for _, annotations in data:
for dep in annotations['deps']: for dep in annotations["deps"]:
parser.add_label(dep) parser.add_label(dep)
def _add_textcat_label(textcat, data): def _add_textcat_label(textcat, data):
for _, annotations in data: for _, annotations in data:
for cat in annotations['cats']: for cat in annotations["cats"]:
textcat.add_label(cat) textcat.add_label(cat)
def _optimize( def _optimize(nlp, component: str, data: List, rehearse: bool):
nlp,
component: str,
data: List,
rehearse: bool
):
"""Run either train or rehearse.""" """Run either train or rehearse."""
pipe = nlp.get_pipe(component) pipe = nlp.get_pipe(component)
if component == 'ner': if component == "ner":
_add_ner_label(pipe, data) _add_ner_label(pipe, data)
elif component == 'tagger': elif component == "tagger":
_add_tagger_label(pipe, data) _add_tagger_label(pipe, data)
elif component == 'parser': elif component == "parser":
_add_tagger_label(pipe, data) _add_tagger_label(pipe, data)
elif component == 'textcat_multilabel': elif component == "textcat_multilabel":
_add_textcat_label(pipe, data) _add_textcat_label(pipe, data)
else: else:
raise NotImplementedError raise NotImplementedError
@ -160,7 +203,7 @@ def _optimize(
return nlp return nlp
@pytest.mark.parametrize("component", ['ner', 'tagger', 'parser', 'textcat_multilabel']) @pytest.mark.parametrize("component", ["ner", "tagger", "parser", "textcat_multilabel"])
def test_rehearse(component): def test_rehearse(component):
nlp = spacy.blank("en") nlp = spacy.blank("en")
nlp.add_pipe(component) nlp.add_pipe(component)

4
spacy/training/converters/conllu_to_docs.py
View File

@ -253,7 +253,9 @@ def conllu_sentence_to_doc(
heads=heads, heads=heads,
) )
if set_ents: if set_ents:
doc_x.ents = [Span(doc_x, ent.start, ent.end, label=ent.label) for ent in doc.ents] doc_x.ents = [
Span(doc_x, ent.start, ent.end, label=ent.label) for ent in doc.ents
]
return doc_x return doc_x