com.johnsnowlabs.nlp.annotators
seq2seq
MarianTransformer: Fast Neural Machine Translation
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:
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.
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 .]| +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
This is the companion object of MarianTransformer.
This is the companion object of MarianTransformer. Please refer to that class for the documentation.
This is the companion object of T5Transformer.
This is the companion object of T5Transformer. Please refer to that class for the documentation.
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
pretrainedof the companion object: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