com.johnsnowlabs.nlp.embeddings
BertSentenceEmbeddings
Related Docs:
object BertSentenceEmbeddings
| package embeddings
com.johnsnowlabs.nlp.embeddings
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
Batch size.
Batch size. Large values allows faster processing but requires more memory.
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
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation
Override for additional custom schema checks
Override for additional custom schema checks
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
input annotations columns currently used
get isLong
Max sentence length to process
Gets annotation column name going to generate
Gets annotation column name going to generate
Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type
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
Use Long type instead of Int type for inputs
Max sentence length to process
Batch size.
Batch size. Large values allows faster processing but requires more memory.
Whether to lowercase tokens or not
Whether to lowercase tokens or not
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
Set Embeddings dimensions for the BERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from BERT config file
Set Embeddings dimensions for the BERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from BERT config file
Overrides required annotators column if different than default
Overrides required annotators column if different than default
set isLong
Max sentence length to process
Overrides annotation column name when transforming
Overrides annotation column name when transforming
Vocabulary used to encode the words to ids with WordPieceEncoder
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()
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
BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertSentenceEmbeddingsTestSpec.scala for further reference on how to use this API. Sources:
Sources :
https://arxiv.org/abs/1810.04805
https://github.com/google-research/bert
Paper abstract
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).