John Snow Labs NLU 5.0.3 - Hotfix for removing logs

disable verbose logs by default

John Snow Labs NLU 5.0.2 - Hotfix for Pandas>=2 compatibility

This is a hotfix release, making NLU compatible with pandas>=2.
NLU is now compatible with any pandas>=1.3.5

Databricks-Serve-Endpoint-Mode and Bug-Fixes in John Snow Labs NLU 5.0.1

• fix bug that caused predicted column names to change when saving/reloading a pipe
• fix bug causing some Visual based nlu components to use wrong data types
• New Databricks-Endpoint based inference mode. It is enabled if the env variable DB_ENDPOINT_ENV is present. When enabled, the first row of every pandas dataframe passed to pipe.predict() is checked for parameters. If your dataframe contains of output_level,positions,keep_stranger_features,metadata,multithread,drop_irrelevant_cols,return_spark_df,get_embeddings, the first row of your dataframe is mapped to the corrosponding parameter and used to call pipeline.predict()

Medical Text Generation, ConvNext for image Classification and DistilBert,Bert,Roberta for Zero-Shot Classification in John Snow Labs NLU 5.0.0

We are very excited to announce NLU 5.0.0 has been released!

It comes with ZeroShotClassification models based on Bert, DistilBert, and Roberta architectures.
Additionally Medical Text Generator based on Bio-GPT as-well as a Bart based General Text Generator are now available in NLU.
Finally, ConvNextForImageClassification is an image classifier based on ConvNet models.

---

ConvNextForImageClassification

Tutorial Notebook
ConvNextForImageClassification is an image classifier based on ConvNet models.
The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
Powered by ConvNextForImageClassification
Reference: A ConvNet for the 2020s

New NLU Models:

Language	NLU Reference	Spark NLP Reference	Task	Annotator Class
en	en.classify_image.convnext.tiny	image_classifier_convnext_tiny_224_local	Image Classification	ConvNextImageClassifier
en	en.classify_image.convnext.tiny	image_classifier_convnext_tiny_224_local	Image Classification	ConvNextImageClassifier

---

DistilBertForZeroShotClassification

Tutorial Notebook

DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.
Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.
Powered by DistilBertForZeroShotClassification

New NLU Models:

Language	NLU Reference	Spark NLP Reference	Task	Annotator Class
en	en.distilbert.zero_shot_classifier	distilbert_base_zero_shot_classifier_uncased_mnli	Zero-Shot Classification	DistilBertForZeroShotClassification
tr	tr.distilbert.zero_shot_classifier.multinli	distilbert_base_zero_shot_classifier_turkish_cased_multinli	Zero-Shot Classification	DistilBertForZeroShotClassification
tr	tr.distilbert.zero_shot_classifier.allnli	distilbert_base_zero_shot_classifier_turkish_cased_allnli	Zero-Shot Classification	DistilBertForZeroShotClassification
tr	tr.distilbert.zero_shot_classifier.snli	distilbert_base_zero_shot_classifier_turkish_cased_snli	Zero-Shot Classification	DistilBertForZeroShotClassification

---

BertForZeroShotClassification

Tutorial Notebook
BertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.
Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.
Powered by BertForZeroShotClassification

New NLU Models:

Language	NLU Reference	Spark NLP Reference	Task	Annotator Class
en	en.bert.zero_shot_classifier	bert_base_cased_zero_shot_classifier_xnli	Zero-Shot Classification	BertForZeroShotClassification

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RoBertaForZeroShotClassification

Tutorial Notebook
RoBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.
Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.
Powered by RoBertaForZeroShotClassification

New NLU Models:

Language	NLU Reference	Spark NLP Reference	Task	Annotator Class
en	en.roberta.zero_shot_classifier	roberta_base_zero_shot_classifier_nli	Zero-Shot Classification	RoBertaForZeroShotClassification

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BartTransformer

Tutorial Notebook

The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation.
BART is unique in that it is both bidirectional and auto-regressive, meaning that it can generate text both from left-to-right and from right-to-left. This allows it to capture contextual information from both past and future tokens in a sentence,resulting in more accurate and natural language generation.
The model was trained on a large corpus of text data using a combination of unsupervised and supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the model is fi...

Medical Summarizer Models in John Snow Labs NLU 4.2.2

New Medical Summarizers:

• 'en.summarize.clinical_jsl'
• 'en.summarize.clinical_jsl_augmented'
• 'en.summarize.biomedical_pubmed'
• 'en.summarize.generic_jsl'
• 'en.summarize.clinical_questions'
• 'en.summarize.radiology'
• 'en.summarize.clinical_guidelines_large'
• 'en.summarize.clinical_laymen'

Hotfix Databricks save and reload models in John Snow Labs NLU 4.2.1

Bugfixes for saving and reloading pipelines on databricks

Support for Speech2Text, Images-Classification, Tabular Data, Zero-Shot-NER, via Wav2Vec2, Tapas, VIT , 4000+ New Models, 90+ Languages, in John Snow Labs NLU 4.2.0

Support for Speech2Text, Images-Classification, Tabular Data, Zero-Shot-NER, via Wav2Vec2, Tapas, VIT , 4000+ New Models, 90+ Languages, in John Snow Labs NLU 4.2.0

We are incredibly excited to announce NLU 4.2.0 has been released with new 4000+ models in 90+ languages and support for new 8 Deep Learning Architectures.
4 new tasks are included for the very first time,
Zero-Shot-NER, Automatic Speech Recognition, Image Classification and Table Question Answering powered
by Wav2Vec 2.0, HuBERT, TAPAS, VIT, SWIN, Zero-Shot-NER.

Additionally, CamemBERT based architectures are available for Sequence and Token Classification powered by Spark-NLPs
CamemBertForSequenceClassification and CamemBertForTokenClassification

Automatic Speech Recognition (ASR)

Demo Notebook
Wav2Vec 2.0 and HuBERT enable ASR for the very first time in NLU.
Wav2Vec2 is a transformer model for speech recognition that uses unsupervised pre-training on large amounts of unlabeled speech data to improve the accuracy of automatic speech recognition (ASR) systems. It is based on a self-supervised learning approach that learns to predict masked portions of speech signal, and has shown promising results in reducing the amount of labeled training data required for ASR tasks.

These Models are powered by Spark-NLP's Wav2Vec2ForCTC Annotator

HuBERT models match or surpass the SOTA approaches for speech representation learning for speech recognition, generation, and compression. The Hidden-Unit BERT (HuBERT) approach was proposed for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss.

These Models is powered by Spark-NLP's HubertForCTC Annotator

Usage

You just need an audio-file on disk and pass the path to it or a folder of audio-files.

import nlu
# Let's download an audio file 
!wget https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/audio/samples/wavs/ngm_12484_01067234848.wav
# Let's listen to it 
from IPython.display import Audio
FILE_PATH = "ngm_12484_01067234848.wav"
asr_df = nlu.load('en.speech2text.wav2vec2.v2_base_960h').predict('ngm_12484_01067234848.wav')
asr_df

text
PEOPLE WHO DIED WHILE LIVING IN OTHER PLACES

To test out HuBERT you just need to update the parameter for load()

asr_df = nlu.load('en.speech2text.hubert').predict('ngm_12484_01067234848.wav')
asr_df

Image Classification

Demo Notebook

For the first time ever NLU introduces state-of-the-art image classifiers based on
VIT and Swin giving you access to hundreds of image classifiers for various domains.

Inspired by the Transformer scaling successes in NLP, the researchers experimented with applying a standard Transformer directly to images, with the fewest possible modifications. To do so, images are split into patches and the sequence of linear embeddings of these patches were provided as an input to a Transformer. Image patches were actually treated the same way as tokens (words) in an NLP application. Image classification models were trained in supervised fashion.

You can check Scale Vision Transformers (ViT) Beyond Hugging Face article to learn deeper how ViT works and how it is implemeted in Spark NLP.
This is Powerd by Spark-NLP's VitForImageClassification Annotator

Swin is a hierarchical Transformer whose representation is computed with Shifted windows.
The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection.
This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks
This is powerd by Spark-NLP's Swin For Image Classification
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.

Usage:

# Download an image
os.system('wget https://raw.githubusercontent.com/JohnSnowLabs/nlu/release/4.2.0/tests/datasets/ocr/vit/ox.jpg') 
# Load VIT model and predict on image file
vit = nlu.load('en.classify_image.base_patch16_224').predict('ox.jpg')

Lets download a folder of images and predict on it

!wget -q https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/images/images.zip
import shutil
shutil.unpack_archive("images.zip", "images", "zip")
! ls /content/images/images/

Once we have image data its easy to label it, we just pass the folder with images to nlu.predict()
and NLU will return a pandas DF with one row per image detected

nlu.load('en.classify_image.base_patch16_224').predict('/content/images/images')

To use SWIN we just update the parameter to load()

load('en.classify_image.swin.tiny').predict('/content/images/images')

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Visual Table Question Answering

TapasForQuestionAnswering can load TAPAS Models with a cell selection head and optional aggregation head on top for question-answering tasks on tables (linear layers on top of the hidden-states output to compute logits and optional logits_aggregation), e.g. for SQA, WTQ or WikiSQL-supervised tasks. TAPAS is a BERT-based model specifically designed (and pre-trained) for answering questions about tabular data.

Demo Notebook

Powered by TAPAS: Weakly Supervised Table Parsing via Pre-training

Usage:

First we need a pandas dataframe on for which we want to ask questions. The so called "context"

import pandas as pd 

context_df = pd.DataFrame({
    'name':['Donald Trump','Elon Musk'], 
    'money': ['$100,000,000','$20,000,000,000,000'], 
    'married': ['yes','no'], 
    'age' : ['75','55'] })
context_df

Then we create an array of questions

questions = [
    "Who earns less than 200,000,000?",
    "Who earns more than 200,000,000?",
    "Who earns 100,000,000?",
    "How much money has Donald Trump?",
    "Who is the youngest?",
]
questions

Now Combine the data, pass it to NLU and get answers for your questions

import nlu
# Now we combine both to a tuple and we are done! We can now pass this to the .predict() method
tapas_data  = (context_df, questions)
# Lets load a TAPAS QA model and predict on (context,question). 
# It will give us an aswer for every question in the questions array, based on the context in context_df
answers = nlu.load('en.answer_question.tapas.wtq.large_finetuned').predict(tapas_data)
answers

sentence	tapas_qa_UNIQUE_aggregation	tapas_qa_UNIQUE_answer	tapas_qa_UNIQUE_cell_positions	tapas_qa_UNIQUE_cell_scores	tapas_qa_UNIQUE_origin_question
Who earns less than 200,000,000?	NONE	Donald Trump	[0, 0]	1	Who earns less than 200,000,000?
Who earns more than 200,000,000?	NONE	Elon Musk	[0, 1]	...

OCR Visual Tables into Pandas DataFrames from PDF/DOCX/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

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

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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

NLU Spell	Transformer Class
nlu.load(pdf2table)	PdfToTextTable
nlu.load(ppt2table)	PptToTextTable
nlu.load(doc2table)	DocToTextTable

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.

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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 :

mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear
21	6	160	110	3.9	2.62	16.46	0	1	4
21	6	160	110	3.9	2.875	17.02	0	1	4
22.8	4	108	93	3.85	2.32	18.61	1	1	4
21.4	6	258	110	3.08	3.215	19.44	1	0	3
18.7	8	360	175	3.15	3.44	17.02	0	0	3
13.3	8	350	245	3.73	3.84	15.41	0	0	3
19.2	8	400	175	3.08	3.845	17.05	0	0	3
27.3	4	79	66	4.08	1.935	18.9	1	1	4
26	4	120.3	91	4.43	2.14	16.7	0	1	5
30.4	4	95.1	113	3.77	1.513	16.9	1	1	5
15.8	8	351	264	4.22	3.17	14.5	0	1	5
19.7	6	145	175	3.62	2.77	15.5	0	1	5
15	8	301	335	3.54	3.57	14.6	0	1	5
21.4	4	121	109	4.11	2.78	18.6	1	1	4

Extract Tables from DOC/DOCX files as Pandas DataFrames

Sample DOCX:

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

Output of DOCX Table OCR :

Screen Reader	Responses	Share
JAWS	853	49%
NVDA	238	14%
Window-Eyes	214	12%
System Access	181	10%
VoiceOver	159	9%

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 :

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

and

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
6.7	3.3	5.7	2.5	virginica
...

600 new models for over 75 new languages including Ancient, Dead and Extinct languages, 155 languages total covered, 400% Tokenizer Speedup, 18x USE-Embeddings GPU speedup in John Snow Labs NLU 3.4.4

We are very excited to announce NLU 3.4.4 has been released with over 600 new models, over 75 new languages, and 155 languages covered in total,
400% speedup for tokenizers and 18x speedup of UniversalSentenceEncoder on GPU.

On the general NLP side, we have transformer-based Embeddings and Token Classifiers powered by state of the art CamemBertEmbeddings and DeBertaForTokenClassification based
architectures as well as various new models for
Historical, Ancient,Dead, Extinct, Genetic, and Constructed languages like
Old Church Slavonic, Latin, Sanskrit, Esperanto, Volapük, Coptic, Nahuatl, Ancient Greek (to 1453), Old Russian.
On the healthcare side, we have Portuguese De-identification Models, have NER models for Gene detection and finally RxNorm Sentence resolution model for mapping and extracting pharmaceutical actions (e.g. analgesic, hypoglycemic)
as well as treatments (e.g. backache, diabetes).

For full release notes with all models see
here
or here ,

First-time language models covered

The languages for these models are covered for the very first time ever by NLU.

Number	Language Name(s)	NLU Reference	Spark NLP Reference	Task	Annotator Class	Scope	Language Type
0	Sanskrit	sa.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Ancient
1	Sanskrit	sa.lemma	lemma_vedic	Lemmatization	LemmatizerModel	Individual	Ancient
2	Sanskrit	sa.pos	pos_vedic	Part of Speech Tagging	PerceptronModel	Individual	Ancient
3	Sanskrit	sa.stopwords	stopwords_iso	Stop Words Removal	StopWordsCleaner	Individual	Ancient
4	Volapük	vo.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Constructed
5	Nahuatl languages	nah.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Collective	Genetic
6	Aragonese	an.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
7	Assamese	as.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
8	Asturian, Asturleonese, Bable, Leonese	ast.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
9	Bashkir	ba.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
10	Bavarian	bar.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
11	Bishnupriya	bpy.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
12	Burmese	my.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
13	Cebuano	ceb.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
14	Central Bikol	bcl.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
15	Chechen	ce.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
16	Chuvash	cv.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
17	Corsican	co.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
18	Dhivehi, Divehi, Maldivian	dv.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
19	Egyptian Arabic	arz.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
20	Emiliano-Romagnolo	eml.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
21	Erzya	myv.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
22	Georgian	ka.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
23	Goan Konkani	gom.embed.w2v_cc_300d	w2v_cc_300d	Embeddings	WordEmbeddingsModel	Individual	Living
24	Javanese	jv.embed.distilbert	distilbert_embeddings_javanese_distilbert_small	Embeddings	DistilBertEmbeddings	Individual	Living
25	Javanese	jv.embed.javanese_distilbert_small_imdb	distilbert_embeddings_javanese_distilbert_small_imdb	Embeddings	DistilBertEmbeddings	Individual	Living
26	Javanese	jv.embed.javanese_roberta_small	roberta_embeddings_javanese_roberta_small	Embeddings	RoBertaEmbeddings	Individual	Living
27	Javanese	[jv.embed.javanese_roberta_small_imdb](https://nlp.jo...

Zero-Shot-Relation-Extraction, DeBERTa for Sequence Classification, 150+ new models, 60+ Languages in John Snow Labs NLU 3.4.3

We are very excited to announce NLU 3.4.3 has been released!

This release features new models for Zero-Shot-Relation-Extraction, DeBERTa for Sequence Classification,
Deidentification in French and Italian and
Lemmatizers, Parts of Speech Taggers, and Word2Vec Embeddings for over 66 languages, with 20 languages being covered
for the first time by NLU, including ancient and exotic languages like Ancient Greek, Old Russian,
Old French and much more. Once again we would like to thank our community to make this release possible.

NLU for Healthcare

On the healthcare NLP side, a new ZeroShotRelationExtractionModel is available, which can extract relations between
clinical entities in an unsupervised fashion, no training required!
Additionally, New French and Italian Deidentification models are available for clinical and healthcare domains.
Powerd by the fantastic Spark NLP for helathcare 3.5.0 release

Zero-Shot Relation Extraction

Zero-shot Relation Extraction to extract relations between clinical entities with no training dataset

import nlu

pipe = nlu.load('med_ner.clinical relation.zeroshot_biobert')
# Configure relations to extract
pipe['zero_shot_relation_extraction'].setRelationalCategories({
    "CURE": ["{{TREATMENT}} cures {{PROBLEM}}."],
    "IMPROVE": ["{{TREATMENT}} improves {{PROBLEM}}.", "{{TREATMENT}} cures {{PROBLEM}}."],
    "REVEAL": ["{{TEST}} reveals {{PROBLEM}}."]})
.setMultiLabel(False)
df = pipe.predict("Paracetamol can alleviate headache or sickness. An MRI test can be used to find cancer.")
df[
    'relation', 'relation_confidence', 'relation_entity1', 'relation_entity1_class', 'relation_entity2', 'relation_entity2_class',]
# Results in following table :

relation	relation_confidence	relation_entity1	relation_entity1_class	relation_entity2	relation_entity2_class
REVEAL	0.976004	An MRI test	TEST	cancer	PROBLEM
IMPROVE	0.988195	Paracetamol	TREATMENT	sickness	PROBLEM
IMPROVE	0.992962	Paracetamol	TREATMENT	headache	PROBLEM

New Healthcare Models overview

Language	NLU Reference	Spark NLP Reference	Task	Annotator Class
en	en.relation.zeroshot_biobert	re_zeroshot_biobert	Relation Extraction	ZeroShotRelationExtractionModel
fr	fr.med_ner.deid_generic	ner_deid_generic	De-identification	MedicalNerModel
fr	fr.med_ner.deid_subentity	ner_deid_subentity	De-identification	MedicalNerModel
it	it.med_ner.deid_generic	ner_deid_generic	Named Entity Recognition	MedicalNerModel
it	it.med_ner.deid_subentity	ner_deid_subentity	Named Entity Recognition	MedicalNerModel

NLU general

On the general NLP side we have new transformer based DeBERTa v3 sequence classifiers models fine-tuned in Urdu, French and English for
Sentiment and News classification. Additionally, 100+ Part Of Speech Taggers and Lemmatizers for 66 Languages and for 7
languages new word2vec embeddings, including hi,azb,bo,diq,cy,es,it,
powered by the amazing Spark NLP 3.4.3 release

New Languages covered:

First time languages covered by NLU are :
South Azerbaijani, Tibetan, Dimli, Central Kurdish, Southern Altai,
Scottish Gaelic,Faroese,Literary Chinese,Ancient Greek,
Gothic, Old Russian, Church Slavic,
Old French,Uighur,Coptic,Croatian, Belarusian, Serbian

and their respective ISO-639-3 and ISO 630-2 codes are :
azb,bo,diq,ckb, lt gd, fo,lzh,grc,got,orv,cu,fro,qtd,ug,cop,hr,be,qhe,sr

New NLP Models Overview

Language	NLU Reference	Spark NLP Reference	Task	Annotator Class
en	en.classify.sentiment.imdb.deberta	deberta_v3_xsmall_sequence_classifier_imdb	Text Classification	DeBertaForSequenceClassification
en	en.classify.sentiment.imdb.deberta.small	deberta_v3_small_sequence_classifier_imdb	Text Classification	DeBertaForSequenceClassification
en	en.classify.sentiment.imdb.deberta.base	deberta_v3_base_sequence_classifier_imdb	Text Classification	DeBertaForSequenceClassification
en	en.classify.sentiment.imdb.deberta.large	deberta_v3_large_sequence_classifier_imdb	Text Classification	DeBertaForSequenceClassification
en	en.classify.news.deberta	deberta_v3_xsmall_sequence_classifier_ag_news	Text Classification	DeBertaForSequenceClassification
en	en.classify.news.deberta.small	deberta_v3_small_sequence_classifier_ag_news	Text Classification	DeBertaForSequenceClassification
ur	ur.classify.sentiment.imdb	mdeberta_v3_base_sequence_classifier_imdb	Text Classification	DeBertaForSequenceClassification
fr	fr.classify.allocine	mdeberta_v3_base_sequence_classifier_allocine	Text Classification	DeBertaForSequenceClassification
ur	ur.embed.bert_cased	bert_embeddings_bert_base_ur_cased	Embeddings	BertEmbeddings
fr	fr.embed.bert_5lang_cased	bert_embeddings_bert_base_5lang_cased	Embeddings	BertEmbeddings
de	...