Source code for sparknlp.annotator.seq2seq.t5_transformer
# Copyright 2017-2022 John Snow Labs
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"""Contains classes for the T5Transformer."""
from sparknlp.common import *
[docs]class T5Transformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""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 :meth:`.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
<https://sparknlp.org/models?q=t5>`__.
For extended examples of usage, see the `Examples
<https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/question-answering/Question_Answering_and_Summarization_with_T5.ipynb>`__.
====================== ======================
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
ignoreTokenIds
A list of token ids which are ignored in the decoder's output
Notes
-----
This is a very computationally expensive module especially on larger
sequence. The use of an accelerator such as GPU is recommended.
References
----------
- `Exploring Transfer Learning with T5: the Text-To-Text Transfer
Transformer
<https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html>`__
- `Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer <https://arxiv.org/abs/1910.10683>`__
- https://github.com/google-research/text-to-text-transfer-transformer
**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 .]|
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
"""
name = "T5Transformer"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.DOCUMENT
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
task = Param(Params._dummy(), "task", "Transformer's task, e.g. summarize>", typeConverter=TypeConverters.toString)
minOutputLength = Param(Params._dummy(), "minOutputLength", "Minimum length of the sequence to be generated",
typeConverter=TypeConverters.toInt)
maxOutputLength = Param(Params._dummy(), "maxOutputLength", "Maximum length of output text",
typeConverter=TypeConverters.toInt)
doSample = Param(Params._dummy(), "doSample", "Whether or not to use sampling; use greedy decoding otherwise",
typeConverter=TypeConverters.toBoolean)
temperature = Param(Params._dummy(), "temperature", "The value used to module the next token probabilities",
typeConverter=TypeConverters.toFloat)
topK = Param(Params._dummy(), "topK",
"The number of highest probability vocabulary tokens to keep for top-k-filtering",
typeConverter=TypeConverters.toInt)
topP = Param(Params._dummy(), "topP",
"If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation",
typeConverter=TypeConverters.toFloat)
repetitionPenalty = Param(Params._dummy(), "repetitionPenalty",
"The parameter for repetition penalty. 1.0 means no penalty. See `this paper <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details",
typeConverter=TypeConverters.toFloat)
noRepeatNgramSize = Param(Params._dummy(), "noRepeatNgramSize",
"If set to int > 0, all ngrams of that size can only occur once",
typeConverter=TypeConverters.toInt)
ignoreTokenIds = Param(Params._dummy(), "ignoreTokenIds",
"A list of token ids which are ignored in the decoder's output",
typeConverter=TypeConverters.toListInt)
useCache = Param(Params._dummy(), "useCache", "Cache internal state of the model to improve performance",
typeConverter=TypeConverters.toBoolean)
stopAtEos = Param(
Params._dummy(),
"stopAtEos",
"Stop text generation when the end-of-sentence token is encountered.",
typeConverter=TypeConverters.toBoolean
)
maxNewTokens = Param(
Params._dummy(),
"maxNewTokens",
"Maximum number of new tokens to be generated",
typeConverter=TypeConverters.toInt
)
[docs] def setIgnoreTokenIds(self, value):
"""A list of token ids which are ignored in the decoder's output.
Parameters
----------
value : List[int]
The words to be filtered out
"""
return self._set(ignoreTokenIds=value)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setTask(self, value):
"""Sets the transformer's task, e.g. ``summarize:``.
Parameters
----------
value : str
The transformer's task
"""
return self._set(task=value)
[docs] def setMinOutputLength(self, value):
"""Sets minimum length of the sequence to be generated.
Parameters
----------
value : int
Minimum length of the sequence to be generated
"""
return self._set(minOutputLength=value)
[docs] def setMaxOutputLength(self, value):
"""Sets maximum length of output text.
Parameters
----------
value : int
Maximum length of output text
"""
return self._set(maxOutputLength=value)
[docs] def setStopAtEos(self, b):
"""Stop text generation when the end-of-sentence token is encountered.
Parameters
----------
b : bool
whether to stop at end-of-sentence token or not
"""
return self._set(stopAtEos=b)
[docs] def setMaxNewTokens(self, value):
"""Sets the maximum number of new tokens to be generated
Parameters
----------
value : int
the maximum number of new tokens to be generated
"""
return self._set(maxNewTokens=value)
[docs] def setDoSample(self, value):
"""Sets whether or not to use sampling, use greedy decoding otherwise.
Parameters
----------
value : bool
Whether or not to use sampling; use greedy decoding otherwise
"""
return self._set(doSample=value)
[docs] def setTemperature(self, value):
"""Sets the value used to module the next token probabilities.
Parameters
----------
value : float
The value used to module the next token probabilities
"""
return self._set(temperature=value)
[docs] def setTopK(self, value):
"""Sets the number of highest probability vocabulary tokens to keep for
top-k-filtering.
Parameters
----------
value : int
Number of highest probability vocabulary tokens to keep
"""
return self._set(topK=value)
[docs] def setTopP(self, value):
"""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
----------
value : float
Cumulative probability for vocabulary tokens
"""
return self._set(topP=value)
[docs] def setRepetitionPenalty(self, value):
"""Sets the parameter for repetition penalty. 1.0 means no penalty.
Parameters
----------
value : float
The repetition penalty
References
----------
See `Ctrl: A Conditional Transformer Language Model For Controllable
Generation <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
"""
return self._set(repetitionPenalty=value)
[docs] def setNoRepeatNgramSize(self, value):
"""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
----------
value : int
N-gram size can only occur once
"""
return self._set(noRepeatNgramSize=value)
[docs] def setUseCache(self, value):
"""Cache internal state of the model to improve performance
Parameters
----------
value : bool
Whether or not to use cache
"""
return self._set(useCache=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.T5Transformer", java_model=None):
super(T5Transformer, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
task="",
minOutputLength=0,
maxOutputLength=20,
doSample=False,
temperature=1.0,
topK=50,
topP=1.0,
repetitionPenalty=1.0,
noRepeatNgramSize=0,
ignoreTokenIds=[],
batchSize=1,
stopAtEos=True,
maxNewTokens=512,
useCache=False
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
T5Transformer
The restored model
"""
from sparknlp.internal import _T5Loader
jModel = _T5Loader(folder, spark_session._jsparkSession)._java_obj
return T5Transformer(java_model=jModel)
@staticmethod
[docs] def pretrained(name="t5_small", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "t5_small"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
T5Transformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(T5Transformer, name, lang, remote_loc)