com.johnsnowlabs.nlp.annotators.seq2seq
MarianTransformer
Related Docs:
object MarianTransformer
| package seq2seq
com.johnsnowlabs.nlp.annotators.seq2seq
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
any number of annotations processed for every input annotation. Not necessary one to one relationship
Size of every batch (Default depends on model).
Size of every batch (Default depends on model).
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
requirement for annotators copies
requirement for annotators copies
Override for additional custom schema checks
Override for additional custom schema checks
Size of every batch.
Size of every batch.
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
A list of token ids which are ignored in the decoder's output
Input Annotator Type: DOCUMENT
Input Annotator Type: DOCUMENT
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
A string representing the target language in the form of >>id<< (id = valid target language ID) (Default: "")
A string representing the target language in the form of >>id<< (id = valid target language ID) (Default: "")
langId is only needed if the model generates multi-lingual target language texts. For instance, for a 'en-fr' model this param is not required to be set.
Controls the maximum length for encoder inputs (source language texts) (Default: 40)
Controls the maximum length for decoder outputs (target language texts) (Default: 40)
Output Annotator Type: DOCUMENT
Output Annotator Type: DOCUMENT
Size of every batch.
Size of every batch.
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Overrides annotation column name when transforming
Overrides annotation column name when transforming
It contains TF model signatures for the laded saved model
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Dataset[Row]
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
required internal uid for saving annotator
required internal uid for saving annotator
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
to be validated
True if all the required types are present, else false
Vocabulary used to encode and decode piece tokens generated by SentencePiece.
Vocabulary used to encode and decode piece tokens generated by SentencePiece. This will be set once the model is created and cannot be changed afterwards
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Required input and expected output annotator types
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