sparknlp.annotator.T5Transformer

class sparknlp.annotator.T5Transformer(classname='com.johnsnowlabs.nlp.annotators.seq2seq.T5Transformer', java_model=None)[source]

Bases: sparknlp.common.AnnotatorModel

T5: the Text-To-Text Transfer Transformer

T5 reconsiders all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. The text-to-text framework is able to use the same model, loss function, and hyper-parameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). T5 can even apply to regression tasks by training it to predict the string representation of a number instead of the number itself.

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

>>> t5 = T5Transformer.pretrained() \
...     .setTask("summarize:") \
...     .setInputCols(["document"]) \
...     .setOutputCol("summaries")

The default model is "t5_small", if no name is provided. For available pretrained models please see the Models Hub.

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

Input Annotation types

Output Annotation type

DOCUMENT

DOCUMENT

Parameters
configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

task

Transformer’s task, e.g. summarize:

minOutputLength

Minimum length of the sequence to be generated

maxOutputLength

Maximum length of output text

doSample

Whether or not to use sampling; use greedy decoding otherwise

temperature

The value used to module the next token probabilities

topK

The number of highest probability vocabulary tokens to keep for top-k-filtering

topP

Top cumulative probability for vocabulary tokens

If set to float < 1, only the most probable tokens with probabilities that add up to topP or higher are kept for generation.

repetitionPenalty

The parameter for repetition penalty. 1.0 means no penalty.

noRepeatNgramSize

If set to int > 0, all ngrams of that size can only occur once

Notes

This is a very computationally expensive module especially on larger sequence. The use of an accelerator such as GPU is recommended.

References

Paper Abstract:

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("documents")
>>> t5 = T5Transformer.pretrained("t5_small") \
...     .setTask("summarize:") \
...     .setInputCols(["documents"]) \
...     .setMaxOutputLength(200) \
...     .setOutputCol("summaries")
>>> pipeline = Pipeline().setStages([documentAssembler, t5])
>>> data = spark.createDataFrame([[
...     "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a " +
...     "downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness" +
...     " of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this " +
...     "paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework " +
...     "that converts all text-based language problems into a text-to-text format. Our systematic study compares " +
...     "pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens " +
...     "of language understanding tasks. By combining the insights from our exploration with scale and our new " +
...     "Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering " +
...     "summarization, question answering, text classification, and more. To facilitate future work on transfer " +
...     "learning for NLP, we release our data set, pre-trained models, and code."
... ]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("summaries.result").show(truncate=False)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result                                                                                                                                                                                                        |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, methodologies, and practice .]|
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

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.

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.

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.

setConfigProtoBytes(b)

Sets configProto from tensorflow, serialized into byte array.

setDoSample(value)

Sets whether or not to use sampling, use greedy decoding otherwise.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setMaxOutputLength(value)

Sets maximum length of output text.

setMinOutputLength(value)

Sets minimum length of the sequence to be generated.

setNoRepeatNgramSize(value)

Sets size of n-grams that can only occur once.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setParams()

setRepetitionPenalty(value)

Sets the parameter for repetition penalty.

setTask(value)

Sets the transformer's task, e.g.

setTemperature(value)

Sets the value used to module the next token probabilities.

setTopK(value)

Sets the number of highest probability vocabulary tokens to keep for top-k-filtering.

setTopP(value)

Sets the top cumulative probability for vocabulary tokens.

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

configProtoBytes

doSample

getter_attrs

inputCols

lazyAnnotator

maxOutputLength

minOutputLength

name

noRepeatNgramSize

outputCol

params

Returns all params ordered by name.

repetitionPenalty

task

temperature

topK

topP

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

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

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
T5Transformer

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='t5_small', lang='en', remote_loc=None)[source]

Downloads and loads a pretrained model.

Parameters
namestr, optional

Name of the pretrained model, by default “t5_small”

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
T5Transformer

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.

setConfigProtoBytes(b)[source]

Sets configProto from tensorflow, serialized into byte array.

Parameters
bList[str]

ConfigProto from tensorflow, serialized into byte array

setDoSample(value)[source]

Sets whether or not to use sampling, use greedy decoding otherwise.

Parameters
valuebool

Whether or not to use sampling; use greedy decoding otherwise

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

setMaxOutputLength(value)[source]

Sets maximum length of output text.

Parameters
valueint

Maximum length of output text

setMinOutputLength(value)[source]

Sets minimum length of the sequence to be generated.

Parameters
valueint

Minimum length of the sequence to be generated

setNoRepeatNgramSize(value)[source]

Sets size of n-grams that can only occur once.

If set to int > 0, all ngrams of that size can only occur once.

Parameters
valueint

N-gram size can only occur once

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

setRepetitionPenalty(value)[source]

Sets the parameter for repetition penalty. 1.0 means no penalty.

Parameters
valuefloat

The repetition penalty

References

See Ctrl: A Conditional Transformer Language Model For Controllable Generation for more details.

setTask(value)[source]

Sets the transformer’s task, e.g. summarize:.

Parameters
valuestr

The transformer’s task

setTemperature(value)[source]

Sets the value used to module the next token probabilities.

Parameters
valuefloat

The value used to module the next token probabilities

setTopK(value)[source]

Sets the number of highest probability vocabulary tokens to keep for top-k-filtering.

Parameters
valueint

Number of highest probability vocabulary tokens to keep

setTopP(value)[source]

Sets the top cumulative probability for vocabulary tokens.

If set to float < 1, only the most probable tokens with probabilities that add up to topP or higher are kept for generation.

Parameters
valuefloat

Cumulative probability for vocabulary tokens

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.