NLU: State of the Art Text Mining in Python

nlu.load('ner').predict('Angela Merkel from Germany and the American Donald Trump dont share many opinions')

nlu.load('ner.conll').predict('Angela Merkel from Germany and the American Donald Trump dont share many opinions')

nlu.load('pos').predict('Part of speech assigns each token in a sentence a grammatical label')

nlu.load('emotion').predict('I love NLU!')

nlu.load('sentiment').predict("I hate this guy Sami")

nlu.load('en.classify.trec50').predict('How expensive is the Watch?')

nlu.load('en.classify.fakenews').predict('Unicorns have been sighted on Mars!')

nlu.load('en.classify.cyberbullying').predict('Women belong in the kitchen.') # sorry we really don't mean it

nlu.load('en.classify.spam').predict('Please sign up for this FREE membership it costs $$NO MONEY$$ just your mobile number!')

nlu.load('en.classify.sarcasm').predict('gotta love the teachers who give exams on the day after halloween')

nlu.load('en.sentiment.imdb').predict('The Matrix was a pretty good movie')

nlu.load('en.sentiment.twitter').predict('@elonmusk Tesla stock price is too high imo')

nlu.load('lang').predict(['NLU is an open-source text processing library for advanced natural language processing for the Python.','NLU est une bibliothèque de traitement de texte open source pour le traitement avancé du langage naturel pour les langages de programmation Python.'])

nlu.load('e2e').predict('E2E is a dataset for training generative models')

nlu.load('en.classify.toxic').predict('You are to stupid')

nlu.load('bert').predict('NLU offers the latest embeddings in one line ')

nlu.load('biobert').predict('Biobert was pretrained on a medical dataset')

nlu.load('covidbert').predict('Albert uses a collection of many berts to generate embeddings')

nlu.load('albert').predict('Albert uses a collection of many berts to generate embeddings')

nlu.load('electra').predict('He was suprised by the diversity of NLU')

nlu.load('elmo').predict('Elmo was trained on Left to right masked to learn its embeddings')

nlu.load('xlnet').predict('XLNET computes contextualized word representations using combination of Autoregressive Language Model and Permutation Language Model')

nlu.load('glove').predict('Glove embeddings are generated by aggregating global word-word co-occurrence matrix from a corpus')

#This takes around 10GB RAM, watch out!
nlu.load('bert albert electra elmo xlnet use glove').predict('Get all of them at once! Watch your RAM tough!')

nlu.load('embed_sentence.electra').predict('He was suprised by the diversity of NLU')

nlu.load('use').predict('USE is designed to encode whole sentences and documents into vectors that can be used for text classification, semantic similarity, clustering or oder NLP tasks')

nlu.load('spell').predict('I liek pentut buttr ant jely')

nlu.load('dep.untyped').predict('Untyped Dependencies represent a grammatical tree structure.md')

nlu.load('dep').predict('Typed Dependencies represent a grammatical tree structure.md where every edge has a label')

nlu.load('tokenize').predict('Each word and symbol in a sentence will generate token.')

nlu.load('stemm').predict('NLU can get you the stem of a word')

nlu.load('stopwords').predict('I want you to remove stopwords from this sentence please')

nlu.load('lemma').predict('Lemmatizing generates a less noisy version of the inputted tokens')

nlu.load('norm').predict('@CKL_IT says that #normalizers are pretty useful to clean #structured_strings in #NLU like tweets')

nlu.load('ngram').predict('Wht a wondful day!')

nlu.load('match.datetime').predict('In the years 2000/01/01 to 2010/01/01 a lot of things happened')

# First we load the pipeline
pipe = nlu.load('match.chunks')
# Now we print the info to see at which index which com,ponent is and what parameters we can configure on them 
pipe.generate_class_metadata_table()
# Lets set our Chunker to only match NN
pipe['default_chunker'].setRegexParsers(['<NN>+', '<JJ>+'])
# Now we can predict with the configured pipeline
pipe.predict("Jim and Joe went to the big blue market next to the town hall")

nlu.load('sentence_detector').predict('NLU can detect things. Like beginning and endings of sentences. It can also do much more!', output_level ='sentence')