Feature Transformation – Word2Vec (Estimator)




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


  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 Description
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.


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.


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

See Also

See https://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in Spark.

Other feature transformers: ft_binarizer(), ft_bucketizer(), ft_chisq_selector(), ft_count_vectorizer(), ft_dct(), ft_elementwise_product(), ft_feature_hasher(), ft_hashing_tf(), ft_idf(), ft_imputer(), ft_index_to_string(), ft_interaction(), ft_lsh, ft_max_abs_scaler(), ft_min_max_scaler(), ft_ngram(), ft_normalizer(), ft_one_hot_encoder_estimator(), ft_one_hot_encoder(), ft_pca(), ft_polynomial_expansion(), ft_quantile_discretizer(), ft_r_formula(), ft_regex_tokenizer(), ft_robust_scaler(), ft_sql_transformer(), ft_standard_scaler(), ft_stop_words_remover(), ft_string_indexer(), ft_tokenizer(), ft_vector_assembler(), ft_vector_indexer(), ft_vector_slicer()