com.johnsnowlabs.nlp.embeddings
Doc2VecModel
Related Docs:
object Doc2VecModel
| package 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
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
Number of embedding dimensions (Default depends on model)
Number of embedding dimensions (Default depends on model)
Override for additional custom schema checks
Override for additional custom schema checks
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Input annotator type : TOKEN
Input annotator type : 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
Output annotator type : SENTENCE_EMBEDDINGS
Output annotator type : SENTENCE_EMBEDDINGS
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
Unique identifier for storage (Default: this.uid)
Unique identifier for storage (Default: this.uid)
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
The dimension of codes after transforming from words (> 0) (Default: 100)
Dictionary of words with their vectors
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
Word2Vec model that creates vector representations of words in a text corpus.
The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.
We use Word2Vec implemented in Spark ML. It uses skip-gram model in our implementation and a hierarchical softmax method to train the model. The variable names in the implementation match the original C implementation.
This is the instantiated model of the Doc2VecApproach. For training your own model, please see the documentation of that class.
Pretrained models can be loaded with
pretrainedof the companion object:The default model is
"doc2vec_gigaword_300", if no name is provided.For available pretrained models please see the Models Hub.
Sources :
For the original C implementation, see https://code.google.com/p/word2vec/
For the research paper, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.
Example