Metadata-Version: 2.1
Name: nlu_spark23
Version: 1.1.1rc2
Summary: John Snow Labs NLU provides state of the art algorithms for NLP&NLU with hundreds of pretrained models in 60+ languages. It enables swift and simple development and research with its powerful Pythonic and Keras inspired API. It is powerd by John Snow Labs powerful Spark NLP library.
Home-page: http://nlu.johnsnowlabs.com
Author: John Snow Labs
Author-email: christian@johnsnowlabs.com
License: UNKNOWN
Description: 
        # NLU: The Power of Spark NLP, the Simplicity of Python
        John Snow Labs' NLU is a Python library for applying state-of-the-art text mining, directly on any dataframe, with a single line of code.
        As a facade of the award-winning Spark NLP library, it comes with hundreds of pretrained models in tens of languages - all production-grade, scalable, and trainable.
        
        ## Project's Website
        Take a look at our official Spark NLU page: [https://nlu.johnsnowlabs.com/](https://nlu.johnsnowlabs.com/)  for user documentation and examples
        
        
        
        ## NLU in action 
        <img src="http://ckl-it.de/wp-content/uploads/2020/08/My-Video6.gif" width="1800" height="500"/>
        
        ## Features
        * Tokenization
        * Trainable Word Segmentation
        * Stop Words Removal
        * Token Normalizer
        * Document Normalizer
        * Stemmer
        * Lemmatizer
        * NGrams
        * Regex Matching
        * Text Matching
        * Chunking
        * Date Matcher
        * Sentence Detector
        * Deep Sentence Detector (Deep learning)
        * Dependency parsing (Labeled/unlabeled)
        * Part-of-speech tagging
        * Sentiment Detection (ML models)
        * Spell Checker (ML and DL models)
        * Word Embeddings (GloVe and Word2Vec)
        * BERT Embeddings (TF Hub models)
        * ELMO Embeddings (TF Hub models)
        * ALBERT Embeddings (TF Hub models)
        * XLNet Embeddings
        * Universal Sentence Encoder (TF Hub models)
        * BERT Sentence Embeddings (42 TF Hub models)
        * Sentence Embeddings
        * Chunk Embeddings
        * Unsupervised keywords extraction
        * Language Detection & Identification (up to 375 languages)
        * Multi-class Sentiment analysis (Deep learning)
        * Multi-label Sentiment analysis (Deep learning)
        * Multi-class Text Classification (Deep learning)
        * Neural Machine Translation
        * Text-To-Text Transfer Transformer (Google T5)
        * Named entity recognition (Deep learning)
        * Easy TensorFlow integration
        * GPU Support
        * Full integration with Spark ML functions
        * +710 pre-trained models in +192 languages!
        * +450 pre-trained pipelines in +192 languages!
        * Multi-lingual NER models: Arabic, Chinese, Danish, Dutch, English, Finnish, French, German, Hewbrew, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, and Urdu.
        
        ## Getting Started with NLU 
        To get your hands on the power of NLU, you just need to install it via pip and ensure Java 8 is installed and properly configured. Checkout [Quickstart for more infos](https://nlu.johnsnowlabs.com/docs/en/install)
        ```bash 
        pip install nlu pyspark==2.4.7
        ``` 
        
        ## Loading and predict with any model in 1 line python 
        ```python
        import nlu 
        nlu.load('sentiment').predict('I love NLU! <3') 
        ``` 
        
        ## Loading and predict with multiple models in 1 line 
        
        Get 6 different embeddings in 1 line and use them for downstream data science tasks! 
        
        ```python 
        nlu.load('bert elmo albert xlnet glove use').predict('I love NLU! <3') 
        ``` 
        
        ## What kind of models does NLU provide? 
        NLU provides everything a data scientist might want to wish for in one line of code!  
         - NLU provides everything a data scientist might want to wish for in one line of code!
         - 1000 + pre-trained models
         - 100+ of the latest NLP word embeddings ( BERT, ELMO, ALBERT, XLNET, GLOVE, BIOBERT, ELECTRA, COVIDBERT) and different variations of them
         - 50+ of the latest NLP sentence embeddings ( BERT, ELECTRA, USE) and different variations of them
         - 100+ Classifiers (NER, POS, Emotion, Sarcasm, Questions, Spam)
         - 300+ Supported Languages
        - Summarize Text and Answer Questions with T5
        - Labeled and Unlabeled Dependency parsing
         - Various Text Cleaning and Pre-Processing methods like Stemming, Lemmatizing, Normalizing, Filtering, Cleaning pipelines and more
        
        
        ## Classifiers trained on many different different datasets 
        Choose the right tool for the right task! Whether you analyze movies or twitter, NLU has the right model for you! 
        
        - trec6 classifier 
        - trec10 classifier 
        - spam classifier 
        - fake news classifier 
        - emotion classifier 
        - cyberbullying classifier 
        - sarcasm classifier 
        - sentiment classifier for movies 
        - IMDB Movie Sentiment classifier 
        - Twitter sentiment classifier 
        - NER pretrained on ONTO notes 
        - NER trainer on CONLL 
        - Language classifier for 20 languages on the wiki 20 lang dataset. 
        
        ## Utilities for the Data Science NLU applications 
        Working with text data can sometimes be quite a dirty Job. NLU helps you keep your hands clean by providing lots of components that take away data engineering intensive tasks. 
        
        - Datetime Matcher
        - Pattern Matcher
        - Chunk Matcher
        - Phrases Matcher
        - Stopword Cleaners
        - Pattern Cleaners
        - Slang Cleaner 
        
        ## Where can I see NLUs entire offer? 
        Checkout the [NLU Namespace](https://nlu.johnsnowlabs.com/docs/en/namespace) for everything that NLU has to offer! 
        
        
        
        ## Supported Data Types
        - Pandas DataFrame and Series
        - Spark DataFrames
        - Modin with Ray backend
        - Modin with Dask backend
        - Numpy arrays
        - Strings and lists of strings 
        
        
        Checkout the following notebooks for examples on how to work with NLU.
        
        
        # NLU Demos on Datasets
        - [Kaggle Twitter Airline Sentiment Analysis NLU demo](https://www.kaggle.com/kasimchristianloan/nlu-sentiment-airline-demo)
        - [Kaggle Twitter Airline Emotion Analysis NLU demo](https://www.kaggle.com/kasimchristianloan/nlu-emotion-airline-demo)
        - [Kaggle Twitter COVID Sentiment Analysis NLU demo](https://www.kaggle.com/kasimchristianloan/nlu-covid-sentiment-showcase)
        - [Kaggle Twitter COVID Emotion Analysis nlu demo](https://www.kaggle.com/kasimchristianloan/nlu-covid-emotion-showcase)
        
        
        # NLU component examples
        Checkout the following notebooks for examples on how to work with NLU.
        
        
        ## NLU Training Examples
        ### Binary Class Text Classification training
        - [2 class Finance News sentiment classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_apple_twitter.ipynb)
        - [2 class Reddit comment sentiment classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_reddit.ipynb)
        - [2 class Apple Tweets sentiment classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb)
        - [2 class IMDB Movie sentiment classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb)
        - [2 class twitter classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_twitter.ipynb)
        
        ### Multi Class Text Classification training
        - [5 class WineEnthusiast Wine review classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_wine.ipynb)
        - [3 class Amazon Phone review classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_amazon.ipynb)
        - [5 class Amazon Musical Instruments review classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_musical_instruments.ipynb)
        - [5 class Tripadvisor Hotel review classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_hotel_reviews.ipynb)
        - [5 class Phone review classifier training](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_hotel_reviews.ipynb)
        
        ### Multi Label Text  Classification training
        - [ Train Multi Label Classifier on E2E dataset Demo](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_label_text_classification/NLU_traing_multi_label_classifier_E2e.ipynb)
        - [Train Multi Label  Classifier on Stack Overflow Question Tags dataset Demo](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_label_text_classification/NLU_training_multi_token_label_text_classifier_stackoverflow_tags.ipynb)
        
        ### Named Entity Recognition training (NER)
        - [NER Training example](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/named_entity_recognition/NLU_training_NER_demo.ipynb)
        
        ### Part of Speech tagger training (POS)
        - [POS Training example](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/part_of_speech/NLU_training_POS_demo.ipynb)
        
        ## NLU Applications Examples
        - [Sentence Similarity with Multiple Sentence Embeddings](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/sentence_similarirty_stack_overflow_questions.ipynb)
        - [6 Wordembeddings in 1 line with T-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_multiple_word_embeddings_and_t-SNE_visualization_example.ipynb)
        
        ## NLU Demos on Datasets
        
        - [Kaggle Twitter Airline Sentiment Analysis NLU demo](https://www.kaggle.com/kasimchristianloan/nlu-sentiment-airline-demo)
        - [Kaggle Twitter Airline Emotion Analysis NLU demo](https://www.kaggle.com/kasimchristianloan/nlu-emotion-airline-demo)
        - [Kaggle Twitter COVID Sentiment Analysis NLU demo](https://www.kaggle.com/kasimchristianloan/nlu-covid-sentiment-showcase)
        - [Kaggle Twitter COVID Emotion Analysis nlu demo](https://www.kaggle.com/kasimchristianloan/nlu-covid-emotion-showcase)
        
        
        
        
        ## NLU examples grouped by component
        
        The following are Collab examples which showcase each NLU component and some applications.
        
        ### Named Entity Recognition (NER)
        
        - [NER pretrained on ONTO Notes](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_ONTO_18class_example.ipynb)
        - [NER pretrained on CONLL](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb)
        - [Tokenize, extract POS and NER in Chinese](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/multilingual/chinese_ner_pos_and_tokenization.ipynb)
        - [Tokenize, extract POS and NER in Korean](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/multilingual/korean_ner_pos_and_tokenization.ipynb)
        - [Tokenize, extract POS and NER in Japanese](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/multilingual/japanese_ner_pos_and_tokenization.ipynb)
        - [Aspect based sentiment NER sentiment for restaurants](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/aspect_based_ner_sentiment_restaurants.ipynb)
        
        
        ### Part of speech (POS)
        
        - [POS pretrained on ANC dataset](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/part_of_speech(POS)/NLU_part_of_speech_ANC_example.ipynb)
        - [Tokenize, extract POS and NER in Chinese](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/multilingual/chinese_ner_pos_and_tokenization.ipynb)
        - [Tokenize, extract POS and NER in Korean](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/multilingual/korean_ner_pos_and_tokenization.ipynb)
        - [Tokenize, extract POS and NER in Japanese](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/multilingual/japanese_ner_pos_and_tokenization.ipynb)
        
        ### Sequence2Sequence
        - [Translate between 192+ languages with marian](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/translation_demo.ipynb)
        - [Try out the 18 Tasks like Summarization Question Answering and more on T5](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_tasks_summarize_question_answering_and_more)
        - [T5 Open and Closed Book question answering tutorial](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_question_answering.ipynb)
        
        
        
        ###  Classifiers
        - [Unsupervised Keyword Extraction with YAKE](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/unsupervised_keyword_extraction_with_YAKE.ipynb)
        - [Toxic Text Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/toxic_classification.ipynb)
        - [Twitter Sentiment Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/sentiment_classification.ipynb)
        - [Movie Review Sentiment Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/sentiment_classification_movies.ipynb)
        - [Sarcasm Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/sarcasm_classification.ipynb)
        - [50 Class Questions Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/question_classification.ipynb)
        - [300 Class Languages Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/NLU_language_classification.ipynb)
        - [Fake News Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/fake_news_classification.ipynb)
        - [E2E Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/E2E_classification.ipynb)
        - [Cyberbullying Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/cyberbullying_cassification_for_racism_and_sexism.ipynb)
        - [Spam Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/spam_classification.ipynb)
        - [Emotion Classifier](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/emotion_classification.ipynb)
        
        ### Word Embeddings
        - [BERT, ALBERT, ELMO, ELECTRA, XLNET, GLOVE at once with t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_multiple_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [BERT Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_BERT_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [ALBERT Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_ALBERT_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [ELMO Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_ELMo_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [XLNET Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_XLNET_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [ELECTRA Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_ELECTRA_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [COVIDBERT Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_COVIDBERT_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [BIOBERT Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_BIOBERT_word_embeddings_and_t-SNE_visualization_example.ipynb)
        - [GLOVE Word Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/word_embeddings/NLU_GLOVE_word_embeddings_and_t-SNE_visualization_example.ipynb)
        
        ### Sentence Embeddings
        - [BERT Sentence Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_BERT_sentence_embeddings_and_t-SNE_visualization_Example.ipynb)
        - [ELECTRA Sentence Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_ELECTRA_sentence_embeddings_and_t-SNE_visualization_example.ipynb)
        - [USE Sentence Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_USE_sentence_embeddings_and_t-SNE_visualization_example.ipynb)
        
        ### Sentence Embeddings
        - [BERT Sentence Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_BERT_sentence_embeddings_and_t-SNE_visualization_Example.ipynb)
        - [ELECTRA Sentence Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_ELECTRA_sentence_embeddings_and_t-SNE_visualization_example.ipynb)
        - [USE Sentence Embeddings and t-SNE plotting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_USE_sentence_embeddings_and_t-SNE_visualization_example.ipynb)
        
        
        ### Dependency Parsing
        - [Untyped Dependency Parsing](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/dependency_parsing/NLU_untyped_dependency_parsing_example.ipynb)
        - [Typed Dependency Parsing](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/dependency_parsing/NLU_typed_dependency_parsing_example.ipynb)
        
        
        ### Text Pre Processing and Cleaning
        - [Tokenization](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/NLU_tokenization_example.ipynb)
        - [Stopwords removal](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/NLU_stopwords_removal_example.ipynb)
        - [Stemming](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/NLU_stemmer_example.ipynb)
        - [Lemmatization](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/NLU_lemmatization.ipynb)
        - [Normalizing](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/NLU_normalizer_example.ipynb)
        - [Spell checking](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/NLU_spellchecking_example.ipynb)
        - [Sentence Detecting](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/NLU_sentence_detection_example.ipynb)
        - [Normalize documents](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/text_pre_processing_and_cleaning/document_normalizer_demo.ipynb)
        
        
        ### Chunkers
        - [N Gram](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/chunkers/NLU_n-gram.ipynb)
        - [Entity Chunking](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/chunkers/NLU_chunking_example.ipynb)
        
        
        ### Matchers
        
        - [Date Matcher](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/matchers/NLU_date_matching.ipynb)
        
        
        # Need help? 
        - [Ping us on Slack](https://spark-nlp.slack.com/archives/C0196BQCDPY) 
        - [Post an issue on Github](https://github.com/JohnSnowLabs/nlu/issues)
        
        # Simple NLU Demos
        - [NLU different output levels Demo](https://colab.research.google.com/drive/1C4N3wpC17YzZf9fXHDNAJ5JvSmfbq7zT?usp=sharing)
        
        
        
Keywords: NLP spark development NLU
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
