Feature Transformation - Word2Vec (Estimator)
ft_word2vec
Description
Word2Vec transforms a word into a code for further natural language processing or machine learning process.
Usage
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
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. |
Details
In the case where x
is a tbl_spark
, the estimator fits against x
to obtain a transformer, returning a tbl_spark
.
Value
The object returned depends on the class of x
. If it is a spark_connection
, the function returns a ml_estimator
or a ml_estimator
object. If it is a ml_pipeline
, it will return a pipeline with the transformer or estimator appended to it. If a tbl_spark
, it will return a tbl_spark
with the transformation applied to it.
ml_find_synonyms()
returns a DataFrame of synonyms and cosine similarities
See Also
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_one_hot_encoder_estimator()
, 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()