Source code for deeppavlov.models.embedders.fasttext_embedder

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from logging import getLogger
from typing import Iterator

    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 """"[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()