class_docstr: Implements a Bag-of-Words (BoW) model to derive sentence embeddings.
  
  A weighting can be added to allow the generation of tf-idf vectors. The output vector has the size of the vocab.
class_docstr_tok: ["Implements","a","Bag-of-Words","(","BoW",")","model","to","derive","sentence","embeddings",".","A","weighting","can","be","added","to","allow","the","generation","of","tf-idf","vectors",".","The","output","vector","has","the","size","of","the","vocab","."]
code: def get_sentence_embedding_dimension(self):
      return self.sentence_embedding_dimension
  
file_path: sentence-transformers/sentence_transformers/models/BoW.py
imports: import torch
  from torch import Tensor
  from torch import nn
  from typing import List, Dict
  import os
  import json
  import logging
  import numpy
  from .tokenizer import WhitespaceTokenizer
label_desc: Use image node_id 4 for calling the BoW obj's underlying member method code with example usage: obj.get_sentence_embedding_dimension() and returns: self