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: Load fastText binary model from self.load_path
docstr_tok: ["Load","fastText","binary","model","from","self.load_path"]
code: def load(self) -> None:
      """
      Load fastText binary model from self.load_path
      """
      log.debug(
          f"[loading fastText embeddings from `{self.load_path}`]"
      )
      self.model = fasttext.load_model(str(self.load_path))
      self.dim = self.model.get_dimension()
  
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 2 for calling the FasttextEmbedder obj's underlying member method code with example usage: obj.load() without return types