NLU release notes

NLU Version 4.0.0

OCR Visual Tables into Pandas DataFrames from PDF/DOC(X)/PPT files, 1000+ new state-of-the-art transformer models for Question Answering (QA) for over 30 languages, up to 700% speedup on GPU, 20 Biomedical models for over 8 languages, 50+ Terminology Code Mappers between RXNORM, NDC, UMLS,ICD10, ICDO, UMLS, SNOMED and MESH, Deidentification in Romanian, various Spark NLP helper methods and much more in 1 line of code with John Snow Labs NLU 4.0.0

NLU 4.0 for OCR Overview

On the OCR side, we now support extracting tables from PDF/DOC(X)/PPT files into structured pandas dataframe, making it easier than ever before to analyze bulks of files visually!

Checkout the OCR Tutorial for extracting Tables from Image/PDF/DOC(X) files to see this in action

These models grab all Table data from the files detected and return a list of Pandas DataFrames,
containing Pandas DataFrame for every table detected

This is powerd by John Snow Labs Spark OCR Annotataors for PdfToTextTable, DocToTextTable, PptToTextTable

NLU 4.0 Core Overview

• On the NLU core side we have over 1000+ new state-of-the-art models in over 30 languages for modern extractive transformer-based Question Answering problems powerd by the ALBERT/BERT/DistilBERT/DeBERTa/RoBERTa/Longformer Spark NLP Annotators trained on various SQUAD-like QA datasets for domains like Twitter, Tech, News, Biomedical COVID-19 and in various model subflavors like sci_bert, electra, mini_lm, covid_bert, bio_bert, indo_bert, muril, sapbert, bioformer, link_bert, mac_bert

• Additionally up to 700% speedup transformer-based Word Embeddings on GPU and up to 97% speedup on CPU for tensorflow operations, support for Apple M1 chips, Pyspark 3.2 and 3.3 support. Ontop of this, we are now supporting Apple M1 based architectures and every Pyspark 3.X version, while deprecating support for Pyspark 2.X.

• Finally, NLU-Core features various new helper methods for working with Spark NLP and embellishes now the entire universe of Annotators defined by Spark NLP and Spark NLP for healthcare.

NLU 4.0 for Healthcare Overview

• On the healthcare side NLU features 20 Biomedical models for over 8 languages (English, French, Italian, Portuguese, Romanian, Catalan and Galician) detect entities like HUMAN and SPECIES based on LivingNER corpus

• Romanian models for Deidentification and extracting Medical entities like Measurements, Form, Symptom, Route, Procedure, Disease_Syndrome_Disorder, Score, Drug_Ingredient, Pulse, Frequency, Date, Body_Part, Drug_Brand_Name, Time, Direction, Dosage, Medical_Device, Imaging_Technique, Test, Imaging_Findings, Imaging_Test, Test_Result, Weight, Clinical_Dept and Units with SPELL and SPELL respectively

• English NER models for parsing entities in Clinical Trial Abstracts like Age, AllocationRatio, Author, BioAndMedicalUnit, CTAnalysisApproach, CTDesign, Confidence, Country, DisorderOrSyndrome, DoseValue, Drug, DrugTime, Duration, Journal, NumberPatients, PMID, PValue, PercentagePatients, PublicationYear, TimePoint, Value using en.med_ner.clinical_trials_abstracts.pipe and also Pathogen NER models for Pathogen, MedicalCondition, Medicine with en.med_ner.pathogen and GENE_PROTEIN with en.med_ner.biomedical_bc2gm.pipeline

•  First Public Health Model for Emotional Stress classification It is a PHS-BERT-based model and trained with the Dreaddit dataset using en.classify.stress

• 50 + new Entity Mappers for problems like :

  • Extract section headers in scientific articles and normalize them with en.map_entity.section_headers_normalized
  • Map medical abbreviates to their definitions with en.map_entity.abbreviation_to_definition
  • Map drugs to action and treatments with en.map_entity.drug_to_action_treatment
  • Map drug brand to their National Drug Code (NDC) with en.map_entity.drug_brand_to_ndc
  • Convert between terminologies using en.<START_TERMINOLOGY>_to_<TARGET_TERMINOLOGY>
    • This works for the terminologies rxnorm, ndc, umls, icd10cm, icdo, umls, snomed, mesh
      • snomed_to_icdo
      • snomed_to_icd10cm
      • rxnorm_to_umls
  • powerd by Spark NLP for Healthcares ChunkMapper Annotator

Extract Tables from PDF files as Pandas DataFrames

Sample PDF:

nlu.load('pdf2table').predict('/path/to/sample.pdf')  

Output of PDF Table OCR :

Extract Tables from DOC/DOCX files as Pandas DataFrames

Sample DOCX:

nlu.load('doc2table').predict('/path/to/sample.docx')  

Output of DOCX Table OCR :

Extract Tables from PPT files as Pandas DataFrame

Sample PPT with two tables:

nlu.load('ppt2table').predict('/path/to/sample.docx')  

Output of PPT Table OCR :

and

Span Classifiers for question answering

Albert, Bert, DeBerta, DistilBert, LongFormer, RoBerta, XlmRoBerta based Transformer Architectures are now avaiable for question answering with almost 1000 models avaiable for 35 unique languages powerd by their corrosponding Spark NLP XXXForQuestionAnswering Annotator Classes and in various tuning and dataset flavours.

<lang>.answer_question.<domain>.<datasets>.<annotator_class><tune info>.by_<username> If multiple datasets or tune parameters are defined , they are connected with a _ .

These substrings define up the <domain> part of the NLU reference

• Legal cuad
• COVID 19 Biomedical biosaq
• Biomedical Literature pubmed
• Twitter tweet
• Wikipedia wiki
• News news
• Tech tech

These substrings define up the <dataset> part of the NLU reference

• Arabic SQUAD ARCD
• Turkish TQUAD
• German GermanQuad
• Indonesian AQG
• Korean KLUE, KORQUAD
• HindiCHAI
• Multi-LingualMLQA
• Multi-Lingualtydiqa
• Multi-Lingualxquad

These substrings define up the <dataset> part of the NLU reference

• Alternative Eval method reqa
• Synthetic Data synqa
• Benchmark / Eval Method ABSA-Bench roberta_absa
• Arabic architecture type soqaol

These substrings define the <annotator_class> substring, if it does not map to a sparknlp annotator

• sci_bert
• electra
• mini_lm
• covid_bert
• bio_bert
• indo_bert
• muril
• sapbert
• bioformer
• link_bert
• mac_bert

These substrings define the <tune_info> substring, if it does not map to a sparknlp annotator

• Train tweaks : multilingual,mini_lm,xtremedistiled,distilled,xtreme,augmented,zero_shot
• Size tweaks xl, xxl, large, base, medium, base, small, tiny, cased, uncased
• Dimension tweaks : 1024d,768d,512d,256d,128d,64d,32d

QA DataFormat

You need to use one of the Data formats below to pass context and question correctly to the model.

# use ||| to seperate question||context
data = 'What is my name?|||My name is Clara and I live in Berkeley'

# pass a tuple (question,context)
data = ('What is my name?','My name is Clara and I live in Berkeley')

# use pandas Dataframe, one column = question, one column=context
data = pd.DataFrame({
					 'question': ['What is my name?'],
					 'context': ["My name is Clara and I live in Berkely"]
					 })


# Get your answers with any of above formats 
nlu.load("en.answer_question.squadv2.deberta").predict(data)

returns :

New NLU helper Methods

You can see all features showcased in the notebook or on the new docs page for Spark NLP utils

nlu.viz(pipe,data)

Visualize input data with an already configured Spark NLP pipeline,
for Algorithms of type (Ner,Assertion, Relation, Resolution, Dependency)
using Spark NLP Display
Automatically infers applicable viz type and output columns to use for visualization.
Example:

# works with Pipeline, LightPipeline, PipelineModel,PretrainedPipeline List[Annotator]
ade_pipeline = PretrainedPipeline('explain_clinical_doc_ade', 'en', 'clinical/models')

text = """I have an allergic reaction to vancomycin.
My skin has be itchy, sore throat/burning/itchy, and numbness in tongue and gums.
I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication."""

nlu.viz(ade_pipeline, text)

returns:

If a pipeline has multiple models candidates that can be used for a viz,
the first Annotator that is vizzable will be used to create viz.
You can specify which type of viz to create with the viz_type parameter

Output columns to use for the viz are automatically deducted from the pipeline, by using the first annotator that provides the correct output type for a specific viz.
You can specify which columns to use for a viz by using the
corresponding ner_col, pos_col, dep_untyped_col, dep_typed_col, resolution_col, relation_col, assertion_col, parameters.

nlu.autocomplete_pipeline(pipe)

Auto-Complete a pipeline or single annotator into a runnable pipeline by harnessing NLU’s DAG Autocompletion algorithm and returns it as NLU pipeline. The standard Spark pipeline is avaiable on the .vanilla_transformer_pipe attribute of the returned nlu pipe

Every Annotator and Pipeline of Annotators defines a DAG of tasks, with various dependencies that must be satisfied in topoligical order. NLU enables the completion of an incomplete DAG by finding or creating a path between the very first input node which is almost always is DocumentAssembler/MultiDocumentAssembler and the very last node(s), which is given by the topoligical sorting the iterable annotators parameter. Paths are created by resolving input features of annotators to the corrrosponding providers with matching storage references.

Example:

# Lets autocomplete the pipeline for a RelationExtractionModel, which as many input columns and sub-dependencies.
from sparknlp_jsl.annotator import RelationExtractionModel
re_model = RelationExtractionModel().pretrained("re_ade_clinical", "en", 'clinical/models').setOutputCol('relation')

text = """I have an allergic reaction to vancomycin.
My skin has be itchy, sore throat/burning/itchy, and numbness in tongue and gums.
I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication."""

nlu_pipe = nlu.autocomplete_pipeline(re_model)
nlu_pipe.predict(text)

returns :

nlu.to_pretty_df(pipe,data)

Annotates a Pandas Dataframe/Pandas Series/Numpy Array/Spark DataFrame/Python List strings /Python String
with given Spark NLP pipeline, which is assumed to be complete and runnable and returns it in a pythonic pandas dataframe format.

Example:

# works with Pipeline, LightPipeline, PipelineModel,PretrainedPipeline List[Annotator]
ade_pipeline = PretrainedPipeline('explain_clinical_doc_ade', 'en', 'clinical/models')

text = """I have an allergic reaction to vancomycin.
My skin has be itchy, sore throat/burning/itchy, and numbness in tongue and gums.
I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication."""

# output is same as nlu.autocomplete_pipeline(re_model).nlu_pipe.predict(text)
nlu.to_pretty_df(ade_pipeline,text)

returns :

Annotators are grouped internally by NLU into output levels token,sentence, document,chunk and relation Same level annotators output columns are zipped and exploded together to create the final output df. Additionally, most keys from the metadata dictionary in the result annotations will be collected and expanded into their own columns in the resulting Dataframe, with special handling for Annotators that encode multiple metadata fields inside of one, seperated by strings like ||| or :::. Some columns are omitted from metadata to reduce total amount of output columns, these can be re-enabled by setting metadata=True

For a given pipeline output level is automatically set to the last anntators output level by default. This can be changed by defining to_preddty_df(pipe,text,output_level='my_level' for levels token,sentence, document,chunk and relation .

nlu.to_nlu_pipe(pipe)

Convert a pipeline or list of annotators into a NLU pipeline making .predict() and .viz() avaiable for every Spark NLP pipeline. Assumes the pipeline is already runnable.

# works with Pipeline, LightPipeline, PipelineModel,PretrainedPipeline List[Annotator]
ade_pipeline = PretrainedPipeline('explain_clinical_doc_ade', 'en', 'clinical/models')

text = """I have an allergic reaction to vancomycin.
My skin has be itchy, sore throat/burning/itchy, and numbness in tongue and gums.
I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication."""

nlu_pipe = nlu.to_nlu_pipe(ade_pipeline)

# Same output as nlu.to_pretty_df(pipe,text) 
nlu_pipe.predict(text)

# same output as nlu.viz(pipe,text)
nlu_pipe.viz(text)

# Acces auto-completed Spark NLP big data pipeline,
nlu_pipe.vanilla_transformer_pipe.transform(spark_df)

returns :

and

4 new Demo Notebooks

These notebooks showcase some of latest classifier models for Banking Queries, Intents in Text, Question and new s classification

• Notebook for Classification of Banking Queries
• Notebook for Classification of Intent in Texts
• Notebook for classification of Similar Questions
• Notebook for Classification of Questions vs Statements
• Notebook for Classification of News into 4 classes

NLU captures every Annotator of Spark NLP and Spark NLP for healthcare

The entire universe of Annotators in Spark NLP and Spark-NLP for healthcare is now embellished by NLU Components by using generalizable annotation extractors methods and configs internally to support enable the new NLU util methods. The following annotator classes are newly captured:

• AssertionFilterer
• ChunkConverter
• ChunkKeyPhraseExtraction
• ChunkSentenceSplitter
• ChunkFiltererApproach
• ChunkFilterer
• ChunkMapperApproach
• ChunkMapperFilterer
• DocumentLogRegClassifierApproach
• DocumentLogRegClassifierModel
• ContextualParserApproach
• ReIdentification
• NerDisambiguator
• NerDisambiguatorModel
• AverageEmbeddings
• EntityChunkEmbeddings
• ChunkMergeApproach
• ChunkMergeApproach
• IOBTagger
• NerChunker
• NerConverterInternalModel
• DateNormalizer
• PosologyREModel
• RENerChunksFilter
• ResolverMerger
• AnnotationMerger
• Router
• Word2VecApproach
• WordEmbeddings
• EntityRulerApproach
• EntityRulerModel
• TextMatcherModel
• BigTextMatcher
• BigTextMatcherModel
• DateMatcher
• MultiDateMatcher
• RegexMatcher
• TextMatcher
• NerApproach
• NerCrfApproach
• NerOverwriter
• DependencyParserApproach
• TypedDependencyParserApproach
• SentenceDetectorDLApproach
• SentimentDetector
• ViveknSentimentApproach
• ContextSpellCheckerApproach
• NorvigSweetingApproach
• SymmetricDeleteApproach
• ChunkTokenizer
• ChunkTokenizerModel
• RecursiveTokenizer
• RecursiveTokenizerModel
• Token2Chunk
• WordSegmenterApproach
• GraphExtraction
• Lemmatizer
• Normalizer

All NLU 4.0 for Healthcare Models

Some examples:

en.rxnorm.umls.mapping

Code:

nlu.load('en.rxnorm.umls.mapping').predict('1161611 315677')

en.ner.clinical_trials_abstracts

Code:

nlu.load('en.ner.clinical_trials_abstracts').predict('A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes.')

Results:

Code:

nlu.load('en.ner.clinical_trials_abstracts').viz('A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes.')

Results:

en.med_ner.pathogen

Code:

nlu.load('en.med_ner.pathogen').predict('Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.')

Results:

Code:

nlu.load('en.med_ner.pathogen').viz('Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.')

Results:

es.med_ner.living_species.roberta

Code:

nlu.load('es.med_ner.living_species.roberta').predict('Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.')

Results:

Code:

nlu.load('es.med_ner.living_species.roberta').viz('Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.')

Results:

All healthcare models added in NLU 4.0 :

All NLU 4.0 Core Models

All core models added in NLU 4.0 : Can be found on the NLU website because of Github Limitations