sparknlp.annotator.DistilBertEmbeddings#

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

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

DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than bert-base-uncased, runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark.

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

>>> embeddings = DistilBertEmbeddings.pretrained() \
...     .setInputCols(["document", "token"]) \
...     .setOutputCol("embeddings")

The default model is "distilbert_base_cased", if no name is provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop. To see which models are compatible and how to import them see Import Transformers into Spark NLP 🚀.

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

Parameters

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

Notes

DistilBERT doesn’t have token_type_ids, you don’t need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or [SEP]).
DistilBERT doesn’t have options to select the input positions (position_ids input). This could be added if necessary though, just let us know if you need this option.

References

The DistilBERT model was proposed in the paper DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Paper Abstract:

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the- edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive biases learned by larger models during pretraining, we introduce a triple loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study.

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 = DistilBertEmbeddings.pretrained() \
...     .setInputCols(["document", "token"]) \
...     .setOutputCol("embeddings") \
...     .setCaseSensitive(True)
>>> 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|
+--------------------------------------------------------------------------------+
|[0.1127224713563919,-0.1982710212469101,0.5360898375511169,-0.272536993026733...|
|[0.35534414649009705,0.13215228915214539,0.40981462597846985,0.14036104083061...|
|[0.328085333108902,-0.06269335001707077,-0.017595693469047546,-0.024373905733...|
|[0.15617232024669647,0.2967822253704071,0.22324979305267334,-0.04568954557180...|
|[0.45411425828933716,0.01173491682857275,0.190129816532135,0.1178255230188369...|
+--------------------------------------------------------------------------------+

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

DistilBertEmbeddings: 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='distilbert_base_cased', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters

namestr, optional: Name of the pretrained model, by default “distilbert_base_cased”
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

DistilBertEmbeddings: 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.DependencyParserModel

sparknlp.annotator.DistilBertForSequenceClassification