Type Members

1.  class MarianTransformer extends AnnotatorModel[MarianTransformer] with HasBatchedAnnotate[MarianTransformer] with WriteTensorflowModel with WriteSentencePieceModel

   MarianTransformer: Fast Neural Machine Translation

   MarianTransformer: Fast Neural Machine Translation

   Marian is an efficient, free Neural Machine Translation framework written in pure C++ with minimal dependencies. It is mainly being developed by the Microsoft Translator team. Many academic (most notably the University of Edinburgh and in the past the Adam Mickiewicz University in Poznań) and commercial contributors help with its development. MarianTransformer uses the models trained by MarianNMT.

   It is currently the engine behind the Microsoft Translator Neural Machine Translation services and being deployed by many companies, organizations and research projects.

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

   val marian = MarianTransformer.pretrained()
     .setInputCols("sentence")
     .setOutputCol("translation")

   The default model is "opus_mt_en_fr", default language is "xx" (meaning multi-lingual), if no values are provided. For available pretrained models please see the Models Hub.

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

   Sources :

   MarianNMT at GitHub

   Marian: Fast Neural Machine Translation in C++

   Paper Abstract:

   We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs. Marian is written entirely in C++. We describe the design of the encoder-decoder framework and demonstrate that a research-friendly toolkit can achieve high training and translation speed.

   Note:

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

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base.DocumentAssembler
   import com.johnsnowlabs.nlp.annotator.SentenceDetectorDLModel
   import com.johnsnowlabs.nlp.annotators.seq2seq.MarianTransformer
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val sentence = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
     .setInputCols("document")
     .setOutputCol("sentence")
   
   val marian = MarianTransformer.pretrained()
     .setInputCols("sentence")
     .setOutputCol("translation")
     .setMaxInputLength(30)
   
   val pipeline = new Pipeline()
     .setStages(Array(
       documentAssembler,
       sentence,
       marian
     ))
   
   val data = Seq("What is the capital of France? We should know this in french.").toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.selectExpr("explode(translation.result) as result").show(false)
   +-------------------------------------+
   |result                               |
   +-------------------------------------+
   |Quelle est la capitale de la France ?|
   |On devrait le savoir en français.    |
   +-------------------------------------+

2.  trait ReadMarianMTTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
3.  trait ReadT5TransformerTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel
4.  trait ReadablePretrainedMarianMTModel extends ParamsAndFeaturesReadable[MarianTransformer] with HasPretrained[MarianTransformer]
5.  trait ReadablePretrainedT5TransformerModel extends ParamsAndFeaturesReadable[T5Transformer] with HasPretrained[T5Transformer]
6.  class T5Transformer extends AnnotatorModel[T5Transformer] with HasSimpleAnnotate[T5Transformer] with ParamsAndFeaturesWritable with WriteTensorflowModel with WriteSentencePieceModel

   T5: the Text-To-Text Transfer Transformer

   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:

   val 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 and the T5TestSpec.

   Sources:

   • Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer
   • Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
   • 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.

   Note:

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

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base.DocumentAssembler
   import com.johnsnowlabs.nlp.annotators.seq2seq.T5Transformer
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("documents")
   
   val t5 = T5Transformer.pretrained("t5_small")
     .setTask("summarize:")
     .setInputCols(Array("documents"))
     .setMaxOutputLength(200)
     .setOutputCol("summaries")
   
   val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))
   
   val data = Seq(
     "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")
   val result = pipeline.fit(data).transform(data)
   
   result.select("summaries.result").show(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 .]|
   +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+