Feature Transformation -- Word2Vec (Estimator)

Word2Vec transforms a word into a code for further natural language processing or machine learning process.

ft_word2vec(x, input_col = NULL, output_col = NULL,
  vector_size = 100, min_count = 5, max_sentence_length = 1000,
  num_partitions = 1, step_size = 0.025, max_iter = 1, seed = NULL,
  uid = random_string("word2vec_"), ...)

ml_find_synonyms(model, word, num)



A spark_connection, ml_pipeline, or a tbl_spark.


The name of the input column.


The name of the output column.


The dimension of the code that you want to transform from words. Default: 100


The minimum number of times a token must appear to be included in the word2vec model's vocabulary. Default: 5


(Spark 2.0.0+) Sets the maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks of up to max_sentence_length size. Default: 1000


Number of partitions for sentences of words. Default: 1


Param for Step size to be used for each iteration of optimization (> 0).


The maximum number of iterations to use.


A random seed. Set this value if you need your results to be reproducible across repeated calls.


A character string used to uniquely identify the feature transformer.


Optional arguments; currently unused.


A fitted Word2Vec model, returned by ft_word2vec().


A word, as a length-one character vector.


Number of words closest in similarity to the given word to find.


The object returned depends on the class of x.

  • spark_connection: When x is a spark_connection, the function returns a ml_transformer, a ml_estimator, or one of their subclasses. The object contains a pointer to a Spark Transformer or Estimator object and can be used to compose Pipeline objects.

  • ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the transformer or estimator appended to the pipeline.

  • tbl_spark: When x is a tbl_spark, a transformer is constructed then immediately applied to the input tbl_spark, returning a tbl_spark

ml_find_synonyms() returns a DataFrame of synonyms and cosine similarities


In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, which is then immediately used to transform x, returning a tbl_spark.

See also