com.johnsnowlabs.nlp.annotators.classifier.dl
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).
Whether to ignore case in index lookups (Default depends on model)
Whether to ignore case in index lookups (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.
Returns labels used to train this model
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Input Annotator Types: DOCUMENT, TOKEN
Input Annotator Types: DOCUMENT, TOKEN
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
Labels used to decode predicted IDs back to string tags
Max sentence length to process (Default: 128)
Holding merges.txt coming from RoBERTa model
Output Annotator Types: WORD_EMBEDDINGS
Output Annotator Types: WORD_EMBEDDINGS
Size of every batch.
Size of every batch.
Whether to lowercase tokens or not
Whether to lowercase tokens or not
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 uid for storing annotator to disk
required uid for storing annotator to disk
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 the words to ids with WordPieceEncoder
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
RoBertaForTokenClassification can load RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
Pretrained models can be loaded with
pretrainedof the companion object:The default model is
"roberta_base_token_classifier_conll03", if no name is provided.For available pretrained models please see the Models Hub.
and the RoBertaForTokenClassificationTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.
Example
Annotators Main Page for a list of transformer based classifiers
RoBertaForTokenClassification for token-level classification