class_docstr: 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: model instance
      tok2emb: dictionary with already embedded tokens
      dim: dimension of embeddings
      pad_zero: whether to pad sequence of tokens with zeros or not
      mean: whether to return one mean embedding vector per sample
      load_path: path with pre-trained fastText binary model
class_docstr_tok: ["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",":","model","instance","tok2emb",":","dictionary","with","already","embedded","tokens","dim",":","dimension","of","embeddings","pad_zero",":","whether","to","pad","sequence","of","tokens","with","zeros","or","not","mean",":","whether","to","return","one","mean","embedding","vector","per","sample","load_path",":","path","with","pre-trained","fastText","binary","model"]
docstr: Initialize embedder with given parameters
docstr_tok: ["Initialize","embedder","with","given","parameters"]
code: def __init__(
      self,
      load_path: Union[str, Path],
      pad_zero: bool = False,
      mean: bool = False,
      **kwargs,
  ) -> None:
      """
      Initialize embedder with given parameters
      """
      super().__init__(save_path=None, load_path=load_path)
      self.tok2emb = {}
      self.pad_zero = pad_zero
      self.mean = mean
      self.dim = None
      self.model = None
      self.load()
  
file_path: DeepPavlov/deeppavlov/models/embedders/abstract_embedder.py
imports: from abc import ABCMeta, abstractmethod
  from logging import getLogger
  from pathlib import Path
  from typing import List, Union, Iterator
  import numpy
  from deeppavlov.core.data.utils import zero_pad
  from deeppavlov.core.models.component import Component
  from deeppavlov.core.models.serializable import Serializable
label_desc: Use image node_id 1 to create a new Embedder object from inherited base classes: Component, Serializable with example: obj = Embedder(load_path, pad_zero, mean)