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: 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
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",":","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"]
docstr: Iterate over all words from fastText model vocabulary
  
  Returns:
      iterator
docstr_tok: ["Iterate","over","all","words","from","fastText","model","vocabulary","Returns",":","iterator"]
code: def __iter__(self) -> Iterator[str]:
      """
      Iterate over all words from fastText model vocabulary
  
      Returns:
          iterator
      """
      yield from self.model.get_words()
  
file_path: DeepPavlov/deeppavlov/models/embedders/fasttext_embedder.py
imports: from logging import getLogger
  from typing import Iterator
  import fasttext
  import numpy
  from deeppavlov.core.common.registry import register
  from deeppavlov.models.embedders.abstract_embedder import Embedder
label_desc: Use image node_id 3 for calling the FasttextEmbedder obj's underlying member method code with example usage: obj.__iter__() without return types