def get_default_component_of_type(missing_component_type,language='en'):
'''
This function returns a default component for a missing component type.
It is used to auto complete pipelines, which are missng required components.
These represents defaults for many applications and should be set wisely.
:param missing_component_type: String which is either just the component type or componenttype@spark_nlp_reference which stems from a models storageref and refers to some pretrained embeddings or model
:return: a NLU component which is a either the default if there is no '@' in the @param missing_component_type or a default component for that particualar type
'''
logger.info('Getting default for missing_component_type=%s', missing_component_type)
if not '@' in missing_component_type:
# get default models if there is no @ in the model name included
if missing_component_type == 'document': return Util('document_assembler')
if missing_component_type == 'sentence': return Util('sentence_detector')
if missing_component_type == 'sentence_embeddings': return Embeddings('use')
if 'token' in missing_component_type: return nlu.components.tokenizer.Tokenizer("default_tokenizer", language=language)
if missing_component_type == 'word_embeddings': return Embeddings(nlu_ref='glove')
if missing_component_type == 'pos': return Classifier(nlu_ref='pos')
if missing_component_type == 'ner': return Classifier(nlu_ref='ner')
if missing_component_type == 'ner_converter': return Util('ner_converter')
if missing_component_type == 'chunk': return nlu.chunker.Chunker()
if missing_component_type == 'ngram': return nlu.chunker.Chunker(nlu_ref='ngram')
if missing_component_type == 'chunk_embeddings': return embeddings_chunker.EmbeddingsChunker()
if missing_component_type == 'unlabeled_dependency': return UnlabledDepParser()
if missing_component_type == 'labled_dependency': return LabledDepParser('dep')
if missing_component_type == 'date': return nlu.Matcher('date')
if missing_component_type == 'ner_converter': return Util('ner_converter')
else:
multi_lang =['ar']
# if there is an @ in the name, we must get some specific pretrained model from the sparknlp reference that should follow after the @
missing_component_type, sparknlp_reference = missing_component_type.split('@')
if 'embed' in missing_component_type:
# TODO RESOLVE MULTI LANG EMBEDS
if language in multi_lang : sparknlp_reference = resolve_multi_lang_embed(language,sparknlp_reference)
return construct_component_from_identifier(language=language, component_type='embed',
nlp_ref=sparknlp_reference)
if 'pos' in missing_component_type or 'ner' in missing_component_type:
return construct_component_from_identifier(language=language, component_type='classifier',
nlp_ref=sparknlp_reference)
if 'chunk_embeddings' in missing_component_type:
return embeddings_chunker.EmbeddingsChunker()
if 'unlabeled_dependency' in missing_component_type or 'dep.untyped' in missing_component_type:
return UnlabledDepParser('dep.untyped')
if 'labled_dependency' in missing_component_type or 'dep.typed' in missing_component_type:
return LabledDepParser('dep.typed')
if 'date' in missing_component_type:
return None
logger.exception("Could not resolve default component type for missing type=%s", missing_component_type)
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_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