from operator import add
from itertools import chain
from sift.models.text import EntityMentions
from sift.util import ngrams
from sift.dataset import ModelBuilder, Model
from sift import logging
log = logging.getLogger()
class EntitySkipGramEmbeddings(ModelBuilder, Model):
""" Learn distributed representations for words and entities in a corpus via skip-gram embedding """
def __init__(self,
dimensions=100,
min_word_count=500,
min_entity_count=10,
entity_prefix='en.wikipedia.org/wiki/',
exclude_words=False,
exclude_entities=False,
workers=4,
coalesce=None,
*args,
**kwargs):
self.dimensions = dimensions
self.min_word_count = min_word_count
self.min_entity_count = min_entity_count
self.filter_target = entity_prefix
self.exclude_words = exclude_words
self.exclude_entities = exclude_entities
from operator import add
from itertools import chain
from sift.models.text import EntityMentions
from sift.util import ngrams
from sift.dataset import ModelBuilder, Model
from sift import logging
log = logging.getLogger()
class EntitySkipGramEmbeddings(ModelBuilder, Model):
""" Learn distributed representations for words and entities in a corpus via skip-gram embedding """
def __init__(
self,
dimensions=100,
min_word_count=500,
min_entity_count=10,
entity_prefix='en.wikipedia.org/wiki/',
exclude_words=False,
exclude_entities=False,
workers=4,
coalesce=None,
*args, **kwargs):
self.dimensions = dimensions
self.min_word_count = min_word_count
self.min_entity_count = min_entity_count
self.filter_target = entity_prefix
self.exclude_words = exclude_words
self.exclude_entities = exclude_entities
self.workers = workers