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)

Arguments

x

A spark_connection, ml_pipeline, or a tbl_spark.

input_col

The name of the input column.

output_col

The name of the output column.

vector_size

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

min_count

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

max_sentence_length

(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

num_partitions

Number of partitions for sentences of words. Default: 1

step_size

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

max_iter

The maximum number of iterations to use.

seed

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

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

model

A fitted Word2Vec model, returned by ft_word2vec().

word

A word, as a length-one character vector.

num

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

Value

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

Details

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