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`