Source code for sparknlp.annotator.seq2seq.bart_transformer
# Copyright 2017-2023 John Snow Labs
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"""Contains classes for the BartTransformer."""
from sparknlp.common import *
[docs]class BartTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension Transformer
The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art
language generation model that was introduced by Facebook AI in 2019. It is based on the
transformer architecture and is designed to handle a wide range of natural language processing
tasks such as text generation, summarization, and machine translation.
BART is unique in that it is both bidirectional and auto-regressive, meaning that it can
generate text both from left-to-right and from right-to-left. This allows it to capture
contextual information from both past and future tokens in a sentence,resulting in more
accurate and natural language generation.
The model was trained on a large corpus of text data using a combination of unsupervised and
supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the
model is first trained on a large unlabeled corpus of text, and then fine-tuned on specific
downstream tasks.
BART has achieved state-of-the-art performance on a wide range of NLP tasks, including
summarization, question-answering, and language translation. Its ability to handle multiple
tasks and its high performance on each of these tasks make it a versatile and valuable tool
for natural language processing applications.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> bart = BartTransformer.pretrained() \\
... .setTask("summarize:") \\
... .setInputCols(["document"]) \\
... .setOutputCol("summaries")
The default model is ``"distilbart_xsum_12_6"``, if no name is provided. For available
pretrained models please see the `Models Hub
<https://sparknlp.org/models?q=bart>`__.
For extended examples of usage, see the `BartTestSpec
<https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/BartTestSpec.scala>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``DOCUMENT``
====================== ======================
Parameters
----------
batchSize
Batch Size, by default `1`.
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
task
Transformer's task, e.g. ``summarize:``, by default `""`.
minOutputLength
Minimum length of the sequence to be generated, by default `0`.
maxOutputLength
Maximum length of output text, by default `20`.
doSample
Whether or not to use sampling; use greedy decoding otherwise, by default `False`.
temperature
The value used to module the next token probabilities, by default `1.0`.
topK
The number of highest probability vocabulary tokens to keep for
top-k-filtering, by default `50`.
beamSize
The number of beam size for beam search, by default `1`.
topP
Top cumulative probability for vocabulary tokens, by default `1.0`.
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, by default `1.0`.
noRepeatNgramSize
If set to int > 0, all ngrams of that size can only occur once, by default `0`.
ignoreTokenIds
A list of token ids which are ignored in the decoder's output, by default `[]`.
useCache
Whether or not to use cache, by default `False`.
Notes
-----
This is a very computationally expensive module especially on larger
sequence. The use of an accelerator such as GPU is recommended.
References
----------
- `Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
<https://arxiv.org/abs/1910.13461>`__
- https://github.com/pytorch/fairseq
**Paper Abstract:**
*We present BART, a denoising autoencoder for pretraining sequence-to-sequence models.
BART is trained by (1) corrupting text with an arbitrary noising function, and (2)
learning a model to reconstruct the original text. It uses a standard Tranformer-based
neural machine translation architecture which, despite its simplicity, can be seen as
generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder),
and many other more recent pretraining schemes. We evaluate a number of noising approaches,
finding the best performance by both randomly shuffling the order of the original sentences
and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for
comprehension tasks. It matches the performance of RoBERTa with comparable training resources
on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue,
question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides
a 1.1 BLEU increase over a back-translation system for machine translation, with only target
language pretraining. We also report ablation experiments that replicate other pretraining
schemes within the BART framework, to better measure which factors most influence end-task performance.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("documents")
>>> bart = BartTransformer.pretrained("distilbart_xsum_12_6") \\
... .setTask("summarize:") \\
... .setInputCols(["documents"]) \\
... .setMaxOutputLength(200) \\
... .setOutputCol("summaries")
>>> pipeline = Pipeline().setStages([documentAssembler, bart])
>>> 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 = "BartTransformer"
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)
beamSize = Param(Params._dummy(), "beamSize",
"The Number of beams for beam search.",
typeConverter=TypeConverters.toInt)
useCache = Param(Params._dummy(), "useCache", "Use caching to enhance performance", typeConverter=TypeConverters.toBoolean)
[docs] def setIgnoreTokenIds(self, value):
"""A list of token ids which are ignored in the decoder's output, by default `[]`.
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:``, by default `""`.
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, by default `0`.
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, by default `20`.
Parameters
----------
value : int
Maximum length of output text
"""
return self._set(maxOutputLength=value)
[docs] def setDoSample(self, value):
"""Sets whether or not to use sampling, use greedy decoding otherwise, by default `False`.
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, by default `1.0`.
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, by default `50`.
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, by default `1.0`.
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, by default `1.0`.
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, by default `0`.
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 setBeamSize(self, value):
"""Sets the number of beam size for beam search, by default `4`.
Parameters
----------
value : int
Number of beam size for beam search
"""
return self._set(beamSize=value)
[docs] def setCache(self, value):
"""Sets whether or not to use caching to enhance performance, by default `False`.
Parameters
----------
value : bool
Whether or not to use caching to enhance performance
"""
return self._set(useCache=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.BartTransformer", java_model=None):
super(BartTransformer, 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,
beamSize=4,
useCache=False,
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session, use_cache=False):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
use_cache: bool
The model uses caching to facilitate performance
Returns
-------
BartTransformer
The restored model
"""
from sparknlp.internal import _BartLoader
jModel = _BartLoader(folder, spark_session._jsparkSession, use_cache)._java_obj
return BartTransformer(java_model=jModel)
@staticmethod
[docs] def pretrained(name="distilbart_xsum_12_6", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "distilbart_xsum_12_6"
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
-------
BartTransformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(BartTransformer, name, lang, remote_loc)