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.
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.
Gets whether to ignore case in tokens for embeddings matching.
Gets embeddings dimension.
Gets current column names of input annotations.
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.
Gets output column name of annotations.
getParam
(paramName)Gets a param by its name.
getParamValue
(paramName)Gets the value of a parameter.
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.
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
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 typeParam
.
- 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.
- 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.