def construct_component_from_pipe_identifier(language, sparknlp_reference):
'''
# creates a list of components from a Spark NLP Pipeline reference
# 1. download pipeline
# 2. unpack pipeline to annotators and create list of nlu components
# 3. return list of nlu components
:param language: language of the pipeline
:param sparknlp_reference: Reference to a spark nlp petrained pipeline
:return: Each element of the SaprkNLP pipeline wrapped as a NLU componed inside of a list
'''
logger.info("Starting Spark NLP to NLU pipeline conversion process")
from sparknlp.pretrained import PretrainedPipeline
if 'language' in sparknlp_reference : language='xx' #special edge case for lang detectors
pipe = PretrainedPipeline(sparknlp_reference, lang=language)
constructed_components = []
for component in pipe.light_model.pipeline_model.stages:
logger.info("Extracting model from Spark NLP pipeline: %s and creating Component", component)
parsed=''
parsed = str(component).split('_')[0].lower()
logger.info("Parsed Component for : %s", parsed)
if 'NerConverter' in component.name : constructed_components.append(Util(component_name='ner_converter', model=component))
elif parsed == 'match': constructed_components.append(nlu.Matcher(model=component))
elif parsed == 'document': constructed_components.append(nlu.Util(model=component))
elif parsed == 'sentence': constructed_components.append(nlu.Util(component_name='sentence_detector',model=component)) # todo differentiate normal and deep detector
elif parsed == 'regex': constructed_components.append(nlu.Matcher(component_name='regex', model=component))
elif parsed == 'text': constructed_components.append(nlu.Matcher(model=component))
elif parsed == 'spell': constructed_components.append(nlu.SpellChecker(model=component))
elif parsed == 'lemmatizer': constructed_components.append(nlu.lemmatizer.Lemmatizer(model=component))
elif parsed == 'normalizer': constructed_components.append(nlu.lemmatizer.Normalizer(model=component))
elif parsed == 'stemmer': constructed_components.append(nlu.stemmer.Stemmer(model=component))
elif parsed == 'pos' or parsed =='language': constructed_components.append(nlu.Classifier(model=component))
elif parsed == 'word': constructed_components.append(nlu.Embeddings(model=component))
elif parsed == 'ner' or parsed == 'nerdlmodel': constructed_components.append(nlu.Classifier(component_name='ner',model=component))
elif parsed == 'dependency': constructed_components.append(nlu.Util(model=component))
elif parsed == 'typed': constructed_components.append(nlu.Util(model=component)) # todo util abuse
elif parsed == 'multi': constructed_components.append(nlu.Util(model=component)) # todo util abuse
elif parsed == 'sentimentdlmodel': constructed_components.append(nlu.Classifier(model=component))
elif parsed in ['universal','bert','albert', 'elmo', 'xlnet', 'glove','electra','covidbert','small_bert',''] : constructed_components.append(nlu.Embeddings(model=component))
elif parsed == 'vivekn': constructed_components.append(nlu.Classifier(component_name='vivekn', model=component))
elif parsed == 'chunker': constructed_components.append(nlu.chunker.Chunker(model=component))
elif parsed == 'ngram': constructed_components.append(nlu.chunker.Chunker(model=component))
elif '2e2' in parsed: constructed_components.append(nlu.Embeddings(model=component))
elif parsed == 'embeddings_chunk': constructed_components.append(embeddings_chunker.EmbeddingsChunker(model=component))
elif parsed == 'stopwords': constructed_components.append(nlu.StopWordsCleaner(model=component))
logger.info("Extracted into NLU Component type : %s", parsed)
if None in constructed_components :
logger.exception("EXCEPTION: Could not infer component type for lang=%s and sparknlp_reference=%s during pipeline conversion,", language,sparknlp_reference)
return None
return constructed_components
def construct_component_from_identifier(language, component_type='', dataset='', component_embeddings='', nlu_ref='',
nlp_ref=''):
'''
Creates a NLU component from a pretrained SparkNLP model reference or Class reference.
Class references will return default pretrained models
:param language: Language of the sparknlp model reference
:param component_type: Class which will be used to instantiate the model
:param dataset: Dataset that the model was trained on
:param component_embeddings: Embedded that the models was traiend on (if any)
:param nlu_ref: Full user request
:param nlp_ref: Full Spark NLP reference
:return: Returns a NLU component which embelished the Spark NLP pretrained model and class for that model
'''
logger.info('Creating singular NLU component for type=%s sparknlp_ref=%s , dataset=%s, language=%s , nlu_ref=%s ',
component_type, nlp_ref, dataset, language, nlu_ref)
try:
if any(
x in NameSpace.seq2seq for x in [nlp_ref, nlu_ref, dataset, component_type, ]):
return Seq2Seq(annotator_class=component_type, language=language, get_default=False, nlp_ref=nlp_ref,configs=dataset)
# if any([component_type in NameSpace.word_embeddings,dataset in NameSpace.word_embeddings, nlu_ref in NameSpace.word_embeddings, nlp_ref in NameSpace.word_embeddings]):
elif any(x in NameSpace.word_embeddings and not x in NameSpace.classifiers for x in
[nlp_ref, nlu_ref, dataset, component_type, ] + dataset.split('_')):
return Embeddings(get_default=False, nlp_ref=nlp_ref, nlu_ref=nlu_ref, language=language)
# elif any([component_type in NameSpace.sentence_embeddings,dataset in NameSpace.sentence_embeddings, nlu_ref in NameSpace.sentence_embeddings, nlp_ref in NameSpace.sentence_embeddings]):
if any(x in NameSpace.sentence_embeddings and not x in NameSpace.classifiers for x in
[nlp_ref, nlu_ref, dataset, component_type, ] + dataset.split('_')):
return Embeddings(get_default=False, nlp_ref=nlp_ref, nlu_ref=nlu_ref, language=language)
elif any(
x in NameSpace.classifiers for x in [nlp_ref, nlu_ref, dataset, component_type, ] + dataset.split('_')):
return Classifier(get_default=False, nlp_ref=nlp_ref, nlu_ref=nlu_ref, language=language)
elif any('spell' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return SpellChecker(annotator_class=component_type, language=language, get_default=True, nlp_ref=nlp_ref,
dataset=dataset)
elif any('dep' in x and not 'untyped' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return LabledDepParser()
elif any('dep.untyped' in x or 'untyped' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return UnlabledDepParser()
elif any('lemma' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return nlu.lemmatizer.Lemmatizer(language=language, nlp_ref=nlp_ref)
elif any('norm' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return nlu.normalizer.Normalizer(nlp_ref=nlp_ref, nlu_ref=nlu_ref)
elif any('clean' in x or 'stopword' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return nlu.StopWordsCleaner(language=language, get_default=False, nlp_ref=nlp_ref)
elif any('sentence_detector' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return NLUSentenceDetector(nlu_ref=nlu_ref, nlp_ref=nlp_ref, language=language)
elif any('match' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return Matcher(nlu_ref=nlu_ref, nlp_ref=nlp_ref)
# THIS NEEDS TO CAPTURE THE WORD SEGMNETER!!!
elif any('tokenize' in x or 'segment_words' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return nlu.tokenizer.Tokenizer(nlp_ref=nlp_ref, nlu_ref=nlu_ref, language=language,get_default=False)
elif any('stem' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return Stemmer()
# supported in future version with auto embed generation
# elif any('embed_chunk' in x for x in [nlp_ref, nlu_ref, dataset, component_type] ):
# return embeddings_chunker.EmbeddingsChunker()
elif any('chunk' in x for x in [nlp_ref, nlu_ref, dataset, component_type]):
return nlu.chunker.Chunker()
elif component_type == 'ngram':
return nlu.chunker.Chunker('ngram')
logger.exception('EXCEPTION: Could not resolve singular Component for type=%s and nlp_ref=%s and nlu_ref=%s',
component_type, nlp_ref, nlu_ref)
return None
except: # if reference is not in namespace and not a component it will cause a unrecoverable crash
logger.exception('EXCEPTION: Could not resolve singular Component for type=%s and nlp_ref=%s and nlu_ref=%s',
component_type, nlp_ref, nlu_ref)
return None
def construct_component_from_pipe_identifier(language, nlp_ref, nlu_ref,path=None):
'''
# creates a list of components from a Spark NLP Pipeline reference
# 1. download pipeline
# 2. unpack pipeline to annotators and create list of nlu components
# 3. return list of nlu components
:param nlu_ref:
:param language: language of the pipeline
:param nlp_ref: Reference to a spark nlp petrained pipeline
:param path: Load pipe from HDD
:return: Each element of the SaprkNLP pipeline wrapped as a NLU componed inside of a list
'''
logger.info("Starting Spark NLP to NLU pipeline conversion process")
from sparknlp.pretrained import PretrainedPipeline, LightPipeline
if 'language' in nlp_ref: language = 'xx' # special edge case for lang detectors
if path == None :
pipe = PretrainedPipeline(nlp_ref, lang=language)
iterable_stages = pipe.light_model.pipeline_model.stages
else :
pipe = LightPipeline(PipelineModel.load(path=path))
iterable_stages = pipe.pipeline_model.stages
constructed_components = []
# for component in pipe.light_model.pipeline_model.stages:
for component in iterable_stages:
logger.info("Extracting model from Spark NLP pipeline: %s and creating Component", component)
parsed = str(component).split('_')[0].lower()
logger.info("Parsed Component for : %s", parsed)
c_name = component.__class__.__name__
if isinstance(component, NerConverter):
constructed_components.append(Util(annotator_class='ner_converter', model=component))
elif parsed in NameSpace.word_embeddings + NameSpace.sentence_embeddings:
constructed_components.append(nlu.Embeddings(model=component))
elif parsed in NameSpace.classifiers:
constructed_components.append(nlu.Classifier(model=component))
elif isinstance(component, MultiClassifierDLModel):
constructed_components.append(nlu.Classifier(model=component, nlp_ref='multiclassifierdl'))
elif isinstance(component, PerceptronModel):
constructed_components.append(nlu.Classifier(nlp_ref='classifierdl', model=component))
elif isinstance(component, (ClassifierDl,ClassifierDLModel)):
constructed_components.append(nlu.Classifier(nlp_ref='classifierdl', model=component))
elif isinstance(component, UniversalSentenceEncoder):
constructed_components.append(nlu.Embeddings(model=component, nlp_ref='use'))
elif isinstance(component, BertEmbeddings):
constructed_components.append(nlu.Embeddings(model=component, nlp_ref='bert'))
elif isinstance(component, AlbertEmbeddings):
constructed_components.append(nlu.Embeddings(model=component, nlp_ref='albert'))
elif isinstance(component, XlnetEmbeddings):
constructed_components.append(nlu.Embeddings(model=component, nlp_ref='xlnet'))
elif isinstance(component, WordEmbeddingsModel):
constructed_components.append(nlu.Embeddings(model=component, nlp_ref='glove'))
elif isinstance(component, ElmoEmbeddings):
constructed_components.append(nlu.Embeddings(model=component, nlp_ref='elmo'))
elif isinstance(component, BertSentenceEmbeddings):
constructed_components.append(nlu.Embeddings(model=component, nlp_ref='bert_sentence'))
elif isinstance(component, UniversalSentenceEncoder):
constructed_components.append(nlu.Embeddings(model=component, nlu_ref='use'))
elif isinstance(component, TokenizerModel) and parsed != 'regex':
constructed_components.append(nlu.Tokenizer(model=component))
elif isinstance(component, TokenizerModel) and parsed == 'regex' :
constructed_components.append(nlu.Tokenizer(model=component, annotator_class='regex_tokenizer'))
elif isinstance(component, DocumentAssembler):
constructed_components.append(nlu.Util(model=component))
elif isinstance(component, SentenceDetectorDLModel):
constructed_components.append(NLUSentenceDetector(annotator_class='deep_sentence_detector', model=component))
elif isinstance(component, (SentenceDetectorDLModel, SentenceDetector)):
constructed_components.append(NLUSentenceDetector(annotator_class='pragmatic_sentence_detector', model=component))
elif isinstance(component, RegexMatcherModel) or parsed == 'match':
constructed_components.append(nlu.Matcher(model=component, annotator_class='regex'))
elif isinstance(component, TextMatcherModel):
constructed_components.append(nlu.Matcher(model=component, annotator_class='text'))
elif isinstance(component, DateMatcher):
constructed_components.append(nlu.Matcher(model=component, annotator_class='date'))
elif isinstance(component, ContextSpellCheckerModel):
constructed_components.append(nlu.SpellChecker(model=component, annotator_class='context'))
elif isinstance(component, SymmetricDeleteModel):
constructed_components.append(nlu.SpellChecker(model=component, annotator_class='symmetric'))
elif isinstance(component, NorvigSweetingModel):
constructed_components.append(nlu.SpellChecker(model=component, annotator_class='norvig'))
elif isinstance(component, LemmatizerModel):
constructed_components.append(nlu.lemmatizer.Lemmatizer(model=component))
elif isinstance(component, NormalizerModel):
constructed_components.append(nlu.normalizer.Normalizer(model=component))
elif isinstance(component, Stemmer):
constructed_components.append(nlu.stemmer.Stemmer(model=component))
elif isinstance(component, (NerDLModel, NerCrfModel)):
component.setIncludeConfidence(True) # Pipes dont always extrat confidences, so here we enable all pipes to extract confidences manually
constructed_components.append(nlu.Classifier(model=component, annotator_class='ner'))
elif isinstance(component, LanguageDetectorDL):
constructed_components.append(nlu.Classifier(model=component, annotator_class='language_detector'))
elif isinstance(component, DependencyParserModel):
constructed_components.append(UnlabledDepParser(model=component))
elif isinstance(component, TypedDependencyParserModel):
constructed_components.append(LabledDepParser(model=component))
elif isinstance(component, MultiClassifierDLModel):
constructed_components.append(nlu.Classifier(model=component, nlp_ref='multiclassifierdl'))
elif isinstance(component, (SentimentDetectorModel,SentimentDLModel)):
constructed_components.append(nlu.Classifier(model=component, nlp_ref='sentimentdl'))
elif isinstance(component, (SentimentDetectorModel,ViveknSentimentModel)):
constructed_components.append(nlu.Classifier(model=component, nlp_ref='vivekn'))
elif isinstance(component, Chunker):
constructed_components.append(nlu.chunker.Chunker(model=component))
elif isinstance(component, NGram):
constructed_components.append(nlu.chunker.Chunker(model=component))
elif isinstance(component, ChunkEmbeddings):
constructed_components.append(embeddings_chunker.EmbeddingsChunker(model=component))
elif isinstance(component, StopWordsCleaner):
constructed_components.append(nlu.StopWordsCleaner(model=component))
elif isinstance(component, (TextMatcherModel, RegexMatcherModel, DateMatcher,MultiDateMatcher)) or parsed == 'match':
constructed_components.append(nlu.Matcher(model=component))
elif isinstance(component,(T5Transformer)):
constructed_components.append(nlu.Seq2Seq(annotator_class='t5', model=component))
elif isinstance(component,(MarianTransformer)):
constructed_components.append(nlu.Seq2Seq(annotator_class='marian', model=component))
else:
logger.exception(
f"EXCEPTION: Could not infer component type for lang={language} and nlp_ref={nlp_ref} and model {component} during pipeline conversion,")
logger.info("USING DEFAULT ANNOTATOR TYPE Lemmatizer to fix issue")
constructed_components.append(nlu.normalizer.Normalizer(model=component))
logger.info(f"Extracted into NLU Component type : {parsed}", )
if None in constructed_components:
logger.exception(
f"EXCEPTION: Could not infer component type for lang={language} and nlp_ref={nlp_ref} during pipeline conversion,")
return None
return constructed_components
def construct_component_from_pipe_identifier(language, nlp_ref, nlu_ref):
'''
# creates a list of components from a Spark NLP Pipeline reference
# 1. download pipeline
# 2. unpack pipeline to annotators and create list of nlu components
# 3. return list of nlu components
:param language: language of the pipeline
:param nlp_ref: Reference to a spark nlp petrained pipeline
:return: Each element of the SaprkNLP pipeline wrapped as a NLU componed inside of a list
'''
logger.info("Starting Spark NLP to NLU pipeline conversion process")
from sparknlp.pretrained import PretrainedPipeline
if 'language' in nlp_ref: language = 'xx' # special edge case for lang detectors
pipe = PretrainedPipeline(nlp_ref, lang=language)
constructed_components = []
for component in pipe.light_model.pipeline_model.stages:
logger.info("Extracting model from Spark NLP pipeline: %s and creating Component", component)
parsed = str(component).split('_')[0].lower()
logger.info("Parsed Component for : %s", parsed)
c_name = component.__class__.__name__
if c_name == 'NerConverter':
constructed_components.append(Util(annotator_class='ner_converter', model=component))
elif parsed in NameSpace.word_embeddings + NameSpace.sentence_embeddings:
constructed_components.append(nlu.Embeddings(model=component))
elif parsed in NameSpace.classifiers:
constructed_components.append(nlu.Classifier(model=component))
elif c_name == 'TokenizerModel' and parsed !='regex':
constructed_components.append(nlu.Tokenizer(model=component))
elif c_name == 'TokenizerModel':
constructed_components.append(nlu.Tokenizer(model=component,annotator_class='regex_tokenizer'))
elif parsed == 'match':
constructed_components.append(nlu.Matcher(model=component))
elif parsed == 'document':
constructed_components.append(nlu.Util(model=component))
elif parsed == 'sentence':
constructed_components.append(nlu.Util(annotator_class='sentence_detector', model=component))
elif parsed == 'regex':
constructed_components.append(nlu.Matcher(model=component, nlu_ref=parsed))
elif parsed == 'date':
constructed_components.append(nlu.Matcher(model=component, nlu_ref=parsed))
elif parsed == 'text':
constructed_components.append(nlu.Matcher(model=component, nlu_ref=parsed))
elif parsed == 'spell':
constructed_components.append(nlu.SpellChecker(model=component))
elif parsed == 'lemmatizer':
constructed_components.append(nlu.lemmatizer.Lemmatizer(model=component))
elif parsed == 'normalizer':
constructed_components.append(nlu.normalizer.Normalizer(model=component))
elif parsed == 'stemmer':
constructed_components.append(nlu.stemmer.Stemmer(model=component))
elif c_name == 'PerceptronModel':
constructed_components.append(nlu.Classifier(annotator_class='classifierdl', model=component))
elif c_name == 'ClassifierDLModel':
constructed_components.append(nlu.Classifier(annotator_class='language_detector', model=component))
elif parsed == 'word':
constructed_components.append(nlu.Embeddings(model=component))
elif parsed == 'ner' or parsed == 'nerdlmodel':
constructed_components.append(nlu.Classifier(model=component))
elif parsed == 'dependency':
constructed_components.append(nlu.Util(model=component))
elif parsed == 'typed':
constructed_components.append(nlu.UnlabledDepParser(model=component))
elif parsed == 'multi':
constructed_components.append(nlu.Classifier(model=component))
elif parsed == 'sentimentdlmodel':
constructed_components.append(nlu.Classifier(model=component))
elif parsed == 'chunker':
constructed_components.append(nlu.chunker.Chunker(model=component))
elif parsed == 'ngram':
constructed_components.append(nlu.chunker.Chunker(model=component))
elif parsed == 'embeddings_chunk':
constructed_components.append(embeddings_chunker.EmbeddingsChunker(model=component))
elif parsed == 'stopwords':
constructed_components.append(nlu.StopWordsCleaner(model=component))
else:
logger.exception(
"EXCEPTION: Could not infer component type for lang=%s and nlp_ref=%s during pipeline conversion,",
language, nlp_ref)
logger.info("USING DEFAULT ANNOTATOR TYPE Lemmatizer to fix issue")
constructed_components.append(nlu.normalizer.Normalizer(model=component))
logger.info("Extracted into NLU Component type : %s", parsed)
if None in constructed_components:
logger.exception(
"EXCEPTION: Could not infer component type for lang=%s and nlp_ref=%s during pipeline conversion,",
language, nlp_ref)
return None
return constructed_components
def construct_component_from_identifier(language, component_type, dataset, component_embeddings, nlu_reference,
sparknlp_reference):
'''
Creates a NLU component from a pretrained SparkNLP model reference or Class reference.
Class references will return default pretrained models
:param language: Language of the sparknlp model reference
:param component_type: Class which will be used to instantiate the model
:param dataset: Dataset that the model was trained on
:param component_embeddings: Embedded that the models was traiend on (if any)
:param nlu_reference: Full user request
:param sparknlp_reference: Full Spark NLP reference
:return: Returns a NLU component which embelished the Spark NLP pretrained model and class for that model
'''
logger.info('Creating singular NLU component for type=%s sparknlp reference=%s , dataset=%s, language=%s ', component_type, sparknlp_reference, dataset, language)
try :
if sparknlp_reference == 'yake':
return Classifier('yake')
elif 'bert' in dataset or component_type == 'embed' or 'albert' in component_type or 'bert' in component_type or 'xlnet' in component_type or 'use' in component_type or 'glove' in component_type or 'elmo' in component_type or 'tfhub_use' in sparknlp_reference\
or 'bert' in sparknlp_reference or 'labse' in sparknlp_reference or component_type =='embed_sentence' or 'electra' in nlu_reference:
if component_type == 'embed' and dataset != '' :
return Embeddings(component_name=dataset, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'embed' : return Embeddings(component_name=sparknlp_reference) #default
else : return Embeddings(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'classify' or 'e2e' in sparknlp_reference:
if component_type == 'classify' and dataset != '' :
return Classifier(component_name=dataset, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
else : return Classifier(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'tokenize':
return nlu.tokenizer.Tokenizer(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'pos':
return Classifier(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'ner' or 'ner_dl' in sparknlp_reference:
return Classifier(component_name='ner', language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'sentiment':
return Classifier(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'emotion':
return Classifier(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'spell':
return SpellChecker(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference, dataset = dataset)
elif component_type == 'dep' and dataset!='untyped' :# There are no trainable dep parsers this gets only default dep
return LabledDepParser(component_name='labeled_dependency_parser', language=language, get_default=True,
sparknlp_reference=sparknlp_reference)
elif component_type == 'dep.untyped' or dataset =='untyped': # There are no trainable dep parsers this gets only default dep
return UnlabledDepParser(component_name='unlabeled_dependency_parser', language=language, get_default=True,
sparknlp_reference=sparknlp_reference)
elif component_type == 'lemma':
return nlu.lemmatizer.Lemmatizer(component_name=component_type, language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'norm':
return nlu.normalizer.Normalizer(component_name='normalizer', language=language, get_default=True,
sparknlp_reference=sparknlp_reference)
elif component_type == 'clean' or component_type == 'stopwords' :
return nlu.StopWordsCleaner( language=language, get_default=False,
sparknlp_reference=sparknlp_reference)
elif component_type == 'sentence_detector':
return NLUSentenceDetector(component_name=component_type, language=language, get_default=True,
sparknlp_reference=sparknlp_reference)
elif component_type == 'match':
return Matcher(component_name=dataset, language=language, get_default=True,
sparknlp_reference=sparknlp_reference)
elif component_type == 'stem' or component_type == 'stemm' or sparknlp_reference == 'stemmer' :
return Stemmer()
elif component_type == 'chunk' :return nlu.chunker.Chunker()
elif component_type == 'ngram' :return nlu.chunker.Chunker('ngram')
elif component_type == 'embed_chunk': return embeddings_chunker.EmbeddingsChunker()
elif component_type == 'regex' or sparknlp_reference =='regex_matcher' : return nlu.Matcher(component_name='regex')
elif component_type == 'text' or sparknlp_reference =='text_matcher' : return nlu.Matcher(component_name='text')
logger.exception('EXCEPTION: Could not resolve singular Component for type=%s and sparknl reference=%s and nlu reference=%s', component_type, sparknlp_reference, nlu_reference)
return None
except : # if reference is not in namespace and not a component it will cause a unrecoverable crash
logger.exception('EXCEPTION: Could not resolve singular Component for type=%s and sparknl reference=%s and nlu reference=%s', component_type, sparknlp_reference, nlu_reference)
return None