def parse(self, text):
tokens = self.tokenizer.tokenize(text)
intent, score = self.get_intent(tokens)
entities = get_entities(text, tokens, self.extractor)
if self.ent_synonyms:
Interpreter.replace_synonyms(entities, self.ent_synonyms)
return {'text': text, 'intent': intent, 'entities': entities, 'confidence': score}
def parse(self, text):
"""Parse the input text, classify it and return an object containing its intent and entities."""
doc = self.nlp(text, entity=False)
intent, probability = self.get_intent(doc)
entities = self.get_entities(doc)
if self.ent_synonyms:
Interpreter.replace_synonyms(entities, self.ent_synonyms)
return {'text': text, 'intent': intent, 'entities': entities, 'confidence': probability}
def parse(self, text):
intent, score = self.get_intent(text)
entities = self.get_entities(text)
if self.ent_synonyms:
Interpreter.replace_synonyms(entities, self.ent_synonyms)
return {
'text': text,
'intent': intent,
'entities': entities,
'confidence': score
}
def load(meta):
"""
:type meta: ModelMetadata
:rtype: MITIEInterpreter
"""
if meta.entity_extractor_path:
if meta.feature_extractor_path is None or not os.path.isfile(meta.feature_extractor_path):
raise Exception("Invalid feature extractor path for MITIE model. Meta data: " + meta)
extractor = named_entity_extractor(
meta.entity_extractor_path, meta.feature_extractor_path)
else:
extractor = None
if meta.intent_classifier_path:
classifier = text_categorizer(
meta.intent_classifier_path, meta.feature_extractor_path)
else:
classifier = None
if meta.entity_synonyms_path:
entity_synonyms = Interpreter.load_synonyms(meta.entity_synonyms_path)
else:
entity_synonyms = None
return MITIEInterpreter(
classifier,
extractor,
entity_synonyms)
def load(meta, nlp, featurizer=None):
"""
:type meta: rasa_nlu.model.Metadata
:type nlp: spacy.language.Language
:type featurizer: None or rasa_nlu.featurizers.spacy_featurizer.SpacyFeaturizer
:rtype: MITIEInterpreter
"""
if meta.entity_extractor_path:
extractor = SpacyEntityExtractor(nlp, meta.entity_extractor_path,
meta.metadata.get("should_fine_tune_spacy_ner"))
else:
extractor = None
if meta.intent_classifier_path:
with open(meta.intent_classifier_path, 'rb') as f:
classifier = cloudpickle.load(f)
else:
classifier = None
if meta.entity_synonyms_path:
entity_synonyms = Interpreter.load_synonyms(meta.entity_synonyms_path)
else:
entity_synonyms = None
if featurizer is None:
featurizer = SpacyFeaturizer(nlp)
return SpacySklearnInterpreter(
classifier,
extractor,
entity_synonyms,
featurizer,
nlp)
def load(meta, featurizer=None):
"""
:type meta: rasa_nlu.model.Metadata
:rtype: MITIESklearnInterpreter
"""
if meta.entity_extractor_path:
extractor = named_entity_extractor(meta.entity_extractor_path)
else:
extractor = None
if featurizer is None:
featurizer = MITIEFeaturizer(meta.feature_extractor_path)
if meta.intent_classifier_path:
with open(meta.intent_classifier_path, 'rb') as f:
classifier = cloudpickle.load(f)
else:
classifier = None
if meta.entity_synonyms_path:
entity_synonyms = Interpreter.load_synonyms(
meta.entity_synonyms_path)
else:
entity_synonyms = None
return MITIESklearnInterpreter(classifier, extractor, featurizer,
entity_synonyms)
def __init__(self,
intent_classifier=None,
entity_extractor=None,
feature_extractor=None,
entity_synonyms=None,
**kwargs):
self.extractor = None
self.classifier = None
if entity_extractor:
self.extractor = named_entity_extractor(entity_extractor, feature_extractor)
if intent_classifier:
self.classifier = text_categorizer(intent_classifier, feature_extractor)
self.tokenizer = MITIETokenizer()
self.ent_synonyms = None
if entity_synonyms:
Interpreter.load_synonyms(entity_synonyms)
def load(meta, featurizer=None):
"""
:type meta: rasa_nlu.model.Metadata
:rtype: MITIEInterpreter
"""
if meta.entity_extractor_path:
extractor = named_entity_extractor(meta.entity_extractor_path)
else:
extractor = None
if meta.intent_classifier_path:
classifier = text_categorizer(meta.intent_classifier_path)
else:
classifier = None
if featurizer is None:
featurizer = MITIEFeaturizer(meta.feature_extractor_path)
if meta.entity_synonyms_path:
entity_synonyms = Interpreter.load_synonyms(
meta.entity_synonyms_path)
else:
entity_synonyms = None
return MITIEInterpreter(classifier, extractor, featurizer,
entity_synonyms)
def test_entity_synonyms():
entities = [{
"entity": "test",
"value": "chines",
"start": 0,
"end": 6
}, {
"entity": "test",
"value": "chinese",
"start": 0,
"end": 6
}, {
"entity": "test",
"value": "china",
"start": 0,
"end": 6
}]
ent_synonyms = {"chines": "chinese", "NYC": "New York City"}
Interpreter.replace_synonyms(entities, ent_synonyms)
assert len(entities) == 3
assert entities[0]["value"] == "chinese"
assert entities[1]["value"] == "chinese"
assert entities[2]["value"] == "china"
def __init__(self,
intent_classifier=None,
entity_extractor=None,
feature_extractor=None,
entity_synonyms=None,
**kwargs):
self.extractor = None
self.classifier = None
if entity_extractor:
self.extractor = named_entity_extractor(entity_extractor,
feature_extractor)
if intent_classifier:
with open(intent_classifier, 'rb') as f:
self.classifier = cloudpickle.load(f)
self.featurizer = MITIEFeaturizer(feature_extractor)
self.tokenizer = MITIETokenizer()
self.ent_synonyms = None
if entity_synonyms:
self.ent_synonyms = Interpreter.load_synonyms(entity_synonyms)
def load(meta, nlp):
"""
:type meta: ModelMetadata
:rtype: MITIEInterpreter
"""
if meta.entity_extractor_path:
extractor = SpacyEntityExtractor(nlp, meta.entity_extractor_path)
else:
extractor = None
if meta.intent_classifier_path:
with open(meta.intent_classifier_path, 'rb') as f:
classifier = cloudpickle.load(f)
else:
classifier = None
if meta.entity_synonyms_path:
entity_synonyms = Interpreter.load_synonyms(
meta.entity_synonyms_path)
else:
entity_synonyms = None
return SpacySklearnInterpreter(classifier, extractor, entity_synonyms,
nlp)
def __init__(self,
entity_extractor=None,
entity_synonyms=None,
intent_classifier=None,
language_name='en',
**kwargs):
self.extractor = None
self.classifier = None
self.ent_synonyms = None
self.nlp = spacy.load(language_name,
parser=False,
entity=False,
matcher=False)
self.featurizer = SpacyFeaturizer(self.nlp)
ensure_proper_language_model(self.nlp)
if intent_classifier:
with open(intent_classifier, 'rb') as f:
self.classifier = cloudpickle.load(f)
if entity_extractor:
self.extractor = SpacyEntityExtractor(self.nlp, entity_extractor)
self.ent_synonyms = Interpreter.load_synonyms(entity_synonyms)
