Word2Vec transforms a word into a code for further natural language processing or machine learning process.
ft_word2vec(x, input_col, output_col, vector_size = 100L, min_count = 5L,
max_sentence_length = 1000L, num_partitions = 1L, step_size = 0.025,
max_iter = 1L, seed = NULL, dataset = 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.
(Optional) A tbl_spark
. If provided, eagerly fit the (estimator)
feature "transformer" against dataset
. See details.
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
When dataset
is provided for an estimator transformer, the function
internally calls ml_fit()
against dataset
. Hence, the methods for
spark_connection
and ml_pipeline
will then return a ml_transformer
and a ml_pipeline
with a ml_transformer
appended, respectively. When
x
is a tbl_spark
, the estimator will be fit against dataset
before
transforming x
.
When dataset
is not specified, the constructor returns a ml_estimator
, and,
in the case where x
is a tbl_spark
, the estimator fits against x
then
to obtain a transformer, which is then immediately used to transform x
.
See http://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
, ft_pca
,
ft_polynomial_expansion
,
ft_quantile_discretizer
,
ft_r_formula
,
ft_regex_tokenizer
,
ft_sql_transformer
,
ft_standard_scaler
,
ft_stop_words_remover
,
ft_string_indexer
,
ft_tokenizer
,
ft_vector_assembler
,
ft_vector_indexer
,
ft_vector_slicer