sparknlp.annotator.XlmRoBertaSentenceEmbeddings#

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

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

Sentence-level embeddings using XLM-RoBERTa. The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco GuzmÃ¡n, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook’s RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. Pretrained models can be loaded with pretrained of the companion object:

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

>>> embeddings = XlmRoBertaSentenceEmbeddings.pretrained() \
...    .setInputCols(["sentence"]) \
...    .setOutputCol("sentence_embeddings")

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

For available pretrained models please see the Models Hub.

Input Annotation types	Output Annotation type
`DOCUMENT`	`SENTENCE_EMBEDDINGS`

Parameters

batchSize: Size of every batch, by default 8
caseSensitive: Whether to ignore case in tokens for embeddings matching, by default False
dimension: Number of embedding dimensions, by default 768
maxSentenceLength: Max sentence length to process, by default 128
configProtoBytes: ConfigProto from tensorflow, serialized into byte array.

References

Unsupervised Cross-lingual Representation Learning at Scale

Paper abstract:

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> sentence = SentenceDetector() \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentence")
>>> embeddings = XlmRoBertaSentenceEmbeddings.pretrained() \
...     .setInputCols(["sentence"]) \
...     .setOutputCol("sentence_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["sentence_embeddings"]) \
...     .setOutputCols("finished_embeddings") \
...     .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     sentence,
...     embeddings,
...     embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["John loves apples. Mary loves oranges. John loves Mary."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[-0.8951074481010437,0.13753940165042877,0.3108254075050354,-1.65693199634552...|
|[-0.6180210709571838,-0.12179657071828842,-0.191165953874588,-1.4497021436691...|
|[-0.822715163230896,0.7568016648292542,-0.1165061742067337,-1.59048593044281,...|
+--------------------------------------------------------------------------------+

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.
`getBatchSize`()	Gets current batch size.
`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`(v)	Sets batch size.
`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.
`setMaxSentenceLength`(value)	Sets max sentence length to process.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setParams`()
`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`
`maxSentenceLength`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`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

getBatchSize()#

Gets current batch size.

Returns

int: Current batch size

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

BertSentenceEmbeddings: 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='sent_xlm_roberta_base', lang='xx', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters

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

Returns

XlmRoBertaSentenceEmbeddings: 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(v)#

Sets batch size.

Parameters

vint: 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

setMaxSentenceLength(value)[source]#

Sets max sentence length to process.

Parameters

valueint: Max sentence length to process

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

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.XlmRoBertaForTokenClassification

sparknlp.annotator.XlnetEmbeddings