# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from logging import getLogger
from typing import Iterator
try:
import fastText
except ModuleNotFoundError as e:
import re
import sys
from pathlib import Path
ft_req_path = Path(__file__).resolve().parents[2].joinpath('requirements', 'fasttext.txt')
packages = ft_req_path.read_text(encoding='utf8').strip()
packages = re.sub(r'\s+', ' ', packages)
raise ModuleNotFoundError(f'{e}\n\nYou can install fastText by running\n'
f'{sys.executable} -m pip install {packages}\n'
'or for your deeppavlov pipeline configuration\n'
f'{sys.executable} -m deeppavlov install <config_path>')
import numpy as np
from overrides import overrides
from deeppavlov.core.common.registry import register
from deeppavlov.models.embedders.abstract_embedder import Embedder
log = getLogger(__name__)
[docs]@register('fasttext')
class FasttextEmbedder(Embedder):
"""
Class implements fastText embedding model
Args:
load_path: path where to load pre-trained embedding model from
pad_zero: whether to pad samples or not
Attributes:
model: fastText model instance
tok2emb: dictionary with already embedded tokens
dim: dimension of embeddings
pad_zero: whether to pad sequence of tokens with zeros or not
load_path: path with pre-trained fastText binary model
"""
def _get_word_vector(self, w: str) -> np.ndarray:
return self.model.get_word_vector(w)
def load(self) -> None:
"""
Load fastText binary model from self.load_path
"""
log.info(f"[loading fastText embeddings from `{self.load_path}`]")
self.model = fastText.load_model(str(self.load_path))
self.dim = self.model.get_dimension()
[docs] @overrides
def __iter__(self) -> Iterator[str]:
"""
Iterate over all words from fastText model vocabulary
Returns:
iterator
"""
yield from self.model.get_words()