Attributes of a fitted Word2VecModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.feature.Word2VecModel — 2 of them — so tab completion is that
allowlist. The server refuses anything else with CONNECT_ML.ATTRIBUTE_NOT_ALLOWED; these
refuse a model of the wrong class before it gets that far, and name the module that would
have taken it.
Allowed by the server but not here: toString is Identifiable's, and tells you less than
the model's own uid. Latu.ML.attribute/2 and Latu.ML.attribute/3 will send any of these
names; it is what comes back that has nowhere to go.
The allowlist is inherited rather than per class: this one is the union of
org.apache.spark.ml.feature.Word2VecModel, org.apache.spark.ml.util.Identifiable.
Summary
Functions
The JVM class these accessors belong to.
Find "num" number of words closest in similarity to "word". word can be a string or vector representation. Returns a dataframe with two fields word and similarity (which gives the cosine similarity).
Returns the vector representation of the words as a dataframe with two fields, word and vector.
Functions
@spec class() :: String.t()
The JVM class these accessors belong to.
@spec find_synonyms( Latu.ML.Model.t(), (String.t() | atom()) | Nx.Tensor.t() | Latu.ML.SparseVector.t(), integer() ) :: Latu.DataFrame.t()
Find "num" number of words closest in similarity to "word". word can be a string or vector representation. Returns a dataframe with two fields word and similarity (which gives the cosine similarity).
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
Arguments
word— either a string or a SparkVector— the value decides whichnum— an integer
@spec get_vectors(Latu.ML.Model.t()) :: Latu.DataFrame.t()
Returns the vector representation of the words as a dataframe with two fields, word and vector.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.