sparknlp.annotator.ElmoEmbeddings#

class sparknlp.annotator.ElmoEmbeddings(classname='com.johnsnowlabs.nlp.embeddings.ElmoEmbeddings', java_model=None)[source]#

Bases: sparknlp.common.AnnotatorModel, sparknlp.common.HasEmbeddingsProperties, sparknlp.common.HasCaseSensitiveProperties, sparknlp.common.HasStorageRef

Word embeddings from ELMo (Embeddings from Language Models), a language model trained on the 1 Billion Word Benchmark.

Note that this is a very computationally expensive module compared to word embedding modules that only perform embedding lookups. The use of an accelerator is recommended.

Pretrained models can be loaded with pretrained() of the companion object:

>>> embeddings = ElmoEmbeddings.pretrained() \
...     .setInputCols(["sentence", "token"]) \
...     .setOutputCol("elmo_embeddings")

The default model is "elmo", if no name is provided.

For available pretrained models please see the Models Hub.

The pooling layer can be set with setPoolingLayer() to the following values:

"word_emb": the character-based word representations with shape [batch_size, max_length, 512].
"lstm_outputs1": the first LSTM hidden state with shape [batch_size, max_length, 1024].
"lstm_outputs2": the second LSTM hidden state with shape [batch_size, max_length, 1024].
"elmo": the weighted sum of the 3 layers, where the weights are trainable. This tensor has shape [batch_size, max_length, 1024].

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types	Output Annotation type
`DOCUMENT, TOKEN`	`WORD_EMBEDDINGS`

Parameters

batchSize: Batch size. Large values allows faster processing but requires more memory, by default 32
dimension: Number of embedding dimensions
caseSensitive: Whether to ignore case in tokens for embeddings matching
configProtoBytes: ConfigProto from tensorflow, serialized into byte array.
poolingLayer: Set ELMO pooling layer to: word_emb, lstm_outputs1, lstm_outputs2, or elmo, by default word_emb

References

https://tfhub.dev/google/elmo/3

Deep contextualized word representations

Paper abstract:

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> embeddings = ElmoEmbeddings.pretrained() \
...     .setPoolingLayer("word_emb") \
...     .setInputCols(["token", "document"]) \
...     .setOutputCol("embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["embeddings"]) \
...     .setOutputCols("finished_embeddings") \
...     .setOutputAsVector(True) \
...     .setCleanAnnotations(False)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     tokenizer,
...     embeddings,
...     embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[6.662458181381226E-4,-0.2541114091873169,-0.6275503039360046,0.5787073969841...|
|[0.19154725968837738,0.22998669743537903,-0.2894386649131775,0.21524395048618...|
|[0.10400570929050446,0.12288510054349899,-0.07056470215320587,-0.246389418840...|
|[0.49932169914245605,-0.12706467509269714,0.30969417095184326,0.2643227577209...|
|[-0.8871506452560425,-0.20039963722229004,-1.0601330995559692,0.0348707810044...|
+--------------------------------------------------------------------------------+

Methods

`__init__`([classname, java_model])	Initialize this instance with a Java model object.
`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
`getCaseSensitive`()	Gets whether to ignore case in tokens for embeddings matching.
`getDimension`()	Gets embeddings dimension.
`getInputCols`()	Gets current column names of input annotations.
`getLazyAnnotator`()	Gets whether Annotator should be evaluated lazily in a RecursivePipeline.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getOutputCol`()	Gets output column name of annotations.
`getParam`(paramName)	Gets a param by its name.
`getParamValue`(paramName)	Gets the value of a parameter.
`getStorageRef`()	Gets unique reference name for identification.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`loadSavedModel`(folder, spark_session)	Loads a locally saved model.
`pretrained`([name, lang, remote_loc])	Downloads and loads a pretrained model.
`read`()	Returns an MLReader instance for this class.
`save`(path)	Save this ML instance to the given path, a shortcut of 'write().save(path)'.
`set`(param, value)	Sets a parameter in the embedded param map.
`setBatchSize`(value)	Sets batch size, by default 32.
`setCaseSensitive`(value)	Sets whether to ignore case in tokens for embeddings matching.
`setConfigProtoBytes`(b)	Sets configProto from tensorflow, serialized into byte array.
`setDimension`(value)	Sets embeddings dimension.
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setParams`()
`setPoolingLayer`(layer)	Sets ELMO pooling layer to: word_emb, lstm_outputs1, lstm_outputs2, or elmo, by default word_emb
`setStorageRef`(value)	Sets unique reference name for identification.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`batchSize`
`caseSensitive`
`configProtoBytes`
`dimension`
`getter_attrs`
`inputCols`
`lazyAnnotator`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`poolingLayer`
`storageRef`

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters: extra – Extra parameters to copy to the new instance
Returns: Copy of this instance

explainParam(param)#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)#

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters: extra – extra param values
Returns: merged param map

getCaseSensitive()#

Gets whether to ignore case in tokens for embeddings matching.

Returns

bool: Whether to ignore case in tokens for embeddings matching

getDimension()#: Gets embeddings dimension.

getInputCols()#: Gets current column names of input annotations.

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)#: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()#: Gets output column name of annotations.

getParam(paramName)#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters

paramNamestr: Name of the parameter

getStorageRef()#

Gets unique reference name for identification.

Returns

str: Unique reference name for identification

hasDefault(param)#: Checks whether a param has a default value.

hasParam(paramName)#: Tests whether this instance contains a param with a given (string) name.

isDefined(param)#: Checks whether a param is explicitly set by user or has a default value.

isSet(param)#: Checks whether a param is explicitly set by user.

classmethod load(path)#: Reads an ML instance from the input path, a shortcut of read().load(path).

static loadSavedModel(folder, spark_session)[source]#

Loads a locally saved model.

Parameters

folderstr: Folder of the saved model
spark_sessionpyspark.sql.SparkSession: The current SparkSession

Returns

ElmoEmbeddings: The restored model

property params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

static pretrained(name='elmo', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters

namestr, optional: Name of the pretrained model, by default “elmo”
langstr, optional: Language of the pretrained model, by default “en”
remote_locstr, optional: Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns

ElmoEmbeddings: The restored model

classmethod read()#: Returns an MLReader instance for this class.

save(path)#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)#: Sets a parameter in the embedded param map.

setBatchSize(value)[source]#

Sets batch size, by default 32.

Parameters

valueint: Batch size

setCaseSensitive(value)#

Sets whether to ignore case in tokens for embeddings matching.

Parameters

valuebool: Whether to ignore case in tokens for embeddings matching

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters

bList[int]: ConfigProto from tensorflow, serialized into byte array

setDimension(value)#

Sets embeddings dimension.

Parameters

valueint: Embeddings dimension

setInputCols(*value)#

Sets column names of input annotations.

Parameters

*valuestr: Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters

valuebool: Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)#

Sets output column name of annotations.

Parameters

valuestr: Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters

paramNamestr: Name of the parameter

setPoolingLayer(layer)[source]#

Sets ELMO pooling layer to: word_emb, lstm_outputs1, lstm_outputs2, or elmo, by default word_emb

Parameters

layerstr: ELMO pooling layer

setStorageRef(value)#

Sets unique reference name for identification.

Parameters

valuestr: Unique reference name for identification

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

Parameters

dataset – input dataset, which is an instance of pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.

Returns

transformed dataset

New in version 1.3.0.

uid#: A unique id for the object.

write()#: Returns an MLWriter instance for this ML instance.

sparknlp.annotator.DocumentNormalizer

sparknlp.annotator.EntityRulerApproach