def neighbors(self):
embeddings = load_embeddings(lang=self.language,
type="sgns",
task="embeddings")
return embeddings.nearest_neighbors(self.string)
def polarity(self):
embeddings = load_embeddings(lang=self.language,
type="",
task="sentiment")
return embeddings.get(self.string, [0])[0]
def vector(self):
embeddings = load_embeddings(lang=self.language,
type="sgns",
task="embeddings")
return embeddings[self.string]
def polarity(self): embeddings = load_embeddings(lang=self.language, type="", task="sentiment") return embeddings.get(self.string, [0])[0]
def neighbors(self):
embeddings = load_embeddings(lang=self.language, type="sgns",
task="embeddings")
return embeddings.nearest_neighbors(self.string)
def vector(self):
embeddings = load_embeddings(lang=self.language, type="sgns",
task="embeddings")
return embeddings[self.string]
def add_polyglot_default():
"""Defining default polyglot models"""
entities = []
load_embeddings()
polyglot_model = [
{
'model_settings': {
'tag': 'I-LOC',
'polyglot_model': 'ner2',
'case_sensitive': True
},
'training': 'finished',
'available': True,
'type': 'default_polyglot',
'description':
'Trained model based on a neural network, detected locations',
'name': 'Detects locations'
},
{
'model_settings': {
'tag': 'I-PER',
'polyglot_model': 'ner2',
'case_sensitive': True
},
'training': 'finished',
'available': True,
'type': 'default_polyglot',
'description':
'Trained model based on a neural network, detected personality',
'name': 'Detects persons'
},
{
'model_settings': {
'tag': 'I-ORG',
'polyglot_model': 'ner2',
},
'training': 'finished',
'available': True,
'type': 'default_polyglot',
'description':
'Trained model based on a neural network, detected organizations',
'name': 'Detects organizations'
},
# {
# 'model_settings': {
# 'tag': 'negative_word',
# 'polyglot_model': 'sentiment2',
# 'case_sensitive': False
# },
# 'training': 'finished',
# 'available': True,
# 'type': 'default_polyglot',
# 'description': 'Trained model based on a neural network, detected negative words',
# 'name': 'negative words'
# },
# {
# 'model_settings': {
# 'tag': 'positive_word',
# 'polyglot_model': 'sentiment2',
# 'case_sensitive': False
# },
# 'training': 'finished',
# 'available': True,
# 'type': 'default_polyglot',
# 'description': 'Trained model based on a neural network, detected positive words',
# 'name': 'positive words'
# },
# {'model_settings': {'tag': 'polarity_sentence', 'polyglot_model': 'sentiment2'},
# 'status': 'train', 'available': True, 'type': 'default_polyglot',
# 'name': 'Polyglot default detected polarity of sentence'},
# {'model_settings': {'tag': 'polarity_text', 'polyglot_model': 'sentiment2'},
# 'status': 'train', 'available': True, 'type': 'default_polyglot',
# 'name': 'Polyglot default detected polarity of document'},
]
mongo = MongoConnection()
for language in SERVER['language']:
# Adding Entities
for model in polyglot_model:
# full_name = Language.from_code(language).name
# if full_name in tools.list_decode(
# downloader.supported_languages(model['model_settings']['polyglot_model'])
# ):
if language in get_supported_languages(
model['model_settings']['polyglot_model']):
model['language'] = language
model['training'] = 'finished'
model['available'] = True
# model['user'] = DEFAULT_USER[language]
entities.append(model)
find_entity = model.copy()
del find_entity['description']
find_model = mongo.default_entity.find_one(find_entity)
if find_model is None:
if '_id' in model:
del model['_id']
try:
# model_id = mongo.default_entity.insert(model)
mongo.default_entity.insert(model)
except Exception:
print(model)
raise
# mongo.users.update_one(
# {'_id': DEFAULT_USER[language]},
# {'$addToSet': {'entity': model_id}},
# upsert=True
# )
return entities
