def __init__(self,
language_name,
max_num_threads=1,
should_fine_tune_spacy_ner=False):
super(self.__class__, self).__init__(language_name, max_num_threads)
self.should_fine_tune_spacy_ner = should_fine_tune_spacy_ner
self.nlp = self._load_nlp_model(language_name,
should_fine_tune_spacy_ner)
self.featurizer = SpacyFeaturizer(self.nlp)
ensure_proper_language_model(self.nlp)
def __init__(self, config, language_name, max_num_threads=1):
self.ensure_language_support(language_name)
self.name = "spacy_sklearn"
self.language_name = language_name
self.max_num_threads = max_num_threads
self.training_data = None
self.nlp = spacy.load(self.language_name, parser=False, entity=False)
self.featurizer = SpacyFeaturizer(self.nlp)
self.intent_classifier = None
self.entity_extractor = None
ensure_proper_language_model(self.nlp)
def __init__(self,
intent_classifier=None,
entity_extractor=None,
entity_synonyms=None,
nlp=None):
self.extractor = entity_extractor
self.classifier = intent_classifier
self.ent_synonyms = entity_synonyms
self.nlp = nlp
self.featurizer = SpacyFeaturizer(nlp)
ensure_proper_language_model(nlp)
def __init__(self,
entity_extractor=None,
intent_classifier=None,
language_name='en',
**kwargs):
self.extractor = None
self.classifier = 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)
def __init__(self, language_name, max_num_threads=1):
super(self.__class__, self).__init__("spacy_sklearn", language_name,
max_num_threads)
self.nlp = spacy.load(self.language_name, parser=False, entity=False)
self.featurizer = SpacyFeaturizer(self.nlp)
ensure_proper_language_model(self.nlp)