nlp_primitives.tensorflow.UniversalSentenceEncoder#

class nlp_primitives.tensorflow.UniversalSentenceEncoder[source]#

Transforms a sentence or short paragraph to a vector using [tfhub model](https://tfhub.dev/google/universal-sentence-encoder/2)

Parameters:

None

Examples

>>> sentences = ["I like to eat pizza", "The roller coaster was built in 1885.", ""]
>>> # universal_sentence_encoder = UniversalSentenceEncoder()  # normal syntax
>>> output = universal_sentence_encoder(sentences)  # defined in test file
>>> len(output)
512
>>> len(output[0])
3
>>> values = output[:3, 0]
>>> [round(x, 4) for x in values]
[0.0178, 0.0616, -0.0089]
__init__()[source]#

Methods

__init__()

flatten_nested_input_types(input_types)

Flattens nested column schema inputs into a single list.

generate_name(base_feature_names)

generate_names(base_feature_names)

get_args_string()

get_arguments()

get_description(input_column_descriptions[, ...])

get_filepath(filename)

get_function()

Attributes

base_of

base_of_exclude

commutative

compatibility

Additional compatible libraries

default_value

Default value this feature returns if no data found.

description_template

input_types

woodwork.ColumnSchema types of inputs

max_stack_depth

name

Name of the primitive

number_output_features

Number of columns in feature matrix associated with this feature

return_type

ColumnSchema type of return

series_library

stack_on

stack_on_exclude

stack_on_self

uses_calc_time

uses_full_dataframe