class_docstr: Defines the Word2Vec embedding technique.
  
  SEE: https://radimrehurek.com/gensim/models/word2vec.html
  
  Attributes:
      _model: The gensim.models.word2vec model.
          Defaults to None.
      kwargs: The keyword arguments dictionary.
          Defaults to { min_count=0 }.
class_docstr_tok: ["Defines","the","Word2Vec","embedding","technique",".","SEE",":","https",":","\/\/radimrehurek.com\/gensim\/models\/word2vec.html","Attributes",":","_model",":","The","gensim.models.word2vec","model",".","Defaults","to","None",".","kwargs",":","The","keyword","arguments","dictionary",".","Defaults","to","{","min_count=0","}","."]
code: def __init__(self, **kwargs):
      self.kwargs = {
          "min_count": 0,
          **kwargs,
      }
      self._model = W2V(**self.kwargs)
  
file_path: pyRDF2Vec/pyrdf2vec/embedders/word2vec.py
imports: from __future__ import annotations
  from typing import List
  import attr
  from gensim.models.word2vec import Word2Vec
  from pyrdf2vec.embedders import Embedder
  from pyrdf2vec.typings import Embeddings, Entities, SWalk
label_desc: Use image node_id 1 to create a new Word2Vec object from inherited base classes: Embedder with example: obj = Word2Vec()