1-liners reference

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

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

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

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

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

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

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

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

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

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

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

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

nlp.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.'])

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

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

nlp.load('yake').predict("NLU is a Python Library for beginners and experts in NLP")

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

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

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

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

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

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

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

nlp.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!
nlp.load('bert albert electra elmo xlnet use glove').predict('Get all of them at once! Watch your RAM tough!')

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

nlp.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')

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

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

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

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

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

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

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

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

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

nlp.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 = nlp.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")

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

pipe = nlp.load('norm_document')
data = '<!DOCTYPE html> <html> <head> <title>Example</title> </head> <body> <p>This is an example of a simple HTML page with one paragraph.</p> </body> </html>'
df = pipe.predict(data,output_level='document')
df

pipe = nlp.load('ja.segment_words')
# japanese for 'Donald Trump and Angela Merkel dont share many opinions'
ja_data = ['ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません']
df = pipe.predict(ja_data, output_level='token')
df

translate_pipe = nlp.load('xx.en.translate_to.de')
df = translate_pipe.predict('Billy likes to go to the mall every sunday')
df

# Set the task on T5
pipe = nlp.load('summarize')

# define Data, add additional tags between sentences
data = [
'''
The belgian duo took to the dance floor on monday night with some friends . manchester united face newcastle in the premier league on wednesday . red devils will be looking for just their second league away win in seven . louis van gaal’s side currently sit two points clear of liverpool in fourth .
''',
'''  Calculus, originally called infinitesimal calculus or "the calculus of infinitesimals", is the mathematical study of continuous change, in the same way that geometry is the study of shape and algebra is the study of generalizations of arithmetic operations. It has two major branches, differential calculus and integral calculus; the former concerns instantaneous rates of change, and the slopes of curves, while integral calculus concerns accumulation of quantities, and areas under or between curves. These two branches are related to each other by the fundamental theorem of calculus, and they make use of the fundamental notions of convergence of infinite sequences and infinite series to a well-defined limit.[1] Infinitesimal calculus was developed independently in the late 17th century by Isaac Newton and Gottfried Wilhelm Leibniz.[2][3] Today, calculus has widespread uses in science, engineering, and economics.[4] In mathematics education, calculus denotes courses of elementary mathematical analysis, which are mainly devoted to the study of functions and limits. The word calculus (plural calculi) is a Latin word, meaning originally "small pebble" (this meaning is kept in medicine – see Calculus (medicine)). Because such pebbles were used for calculation, the meaning of the word has evolved and today usually means a method of computation. It is therefore used for naming specific methods of calculation and related theories, such as propositional calculus, Ricci calculus, calculus of variations, lambda calculus, and process calculus.'''
]


#Predict on text data with T5
pipe.predict(data)

t5 = nlp.load('en.t5.base')
# Set the task on T5
t5['t5'].setTask('stsb ') 

# define Data, add additional tags between sentences
data = [
             
              ''' sentence1:  What attributes would have made you highly desirable in ancient Rome?  
                  sentence2:  How I GET OPPERTINUTY TO JOIN IT COMPANY AS A FRESHER?'
              '''
             ,
             '''  
              sentence1: What was it like in Ancient rome?
              sentence2: 	What was Ancient rome like?
              ''',
              '''  
              sentence1: What was live like as a King in Ancient Rome??
              sentence2: 	What was Ancient rome like?
              '''

             ]



#Predict on text data with T5
t5.predict(data)

pipe = nlp.load('grammar_correctness')
# Set the task on T5
pipe['t5'].setTask('cola sentence: ')
# define Data
data = ['Anna and Mike is going skiing and they is liked is','Anna and Mike like to dance']
#Predict on text data with T5
pipe.predict(data)

nlp.load('answer_question').predict("""
Where did Jebe die?
context: Ghenkis Khan recalled Subtai back to Mongolia soon afterwards,
 and Jebe died on the road back to Samarkand""")
>>> Output: Samarkand

question ='''
What does increased oxygen concentrations in the patient’s lungs displace? 
context: Hyperbaric (high-pressure) medicine uses special oxygen chambers to increase the partial pressure of O 2 around the patient and, when needed, the medical staff. 
Carbon monoxide poisoning, gas gangrene, and decompression sickness (the ’bends’) are sometimes treated using these devices. Increased O 2 concentration in the lungs helps to displace carbon monoxide from the heme group of hemoglobin.
 Oxygen gas is poisonous to the anaerobic bacteria that cause gas gangrene, so increasing its partial pressure helps kill them. Decompression sickness occurs in divers who decompress too quickly after a dive, resulting in bubbles of inert gas, mostly nitrogen and helium, forming in their blood. Increasing the pressure of O 2 as soon as possible is part of the treatment.
'''


#Predict on text data with T5
nlp.load('answer_question').predict(question)
>>> Output: carbon monoxide	

question1 = 'Who is Jack ma?'
question2 = 'Who is founder of Alibaba Group?'
question3 = 'When did Jack Ma re-appear?'
question4 = 'How did Alibaba stocks react?'
question5 = 'Whom did Jack Ma meet?'
question6 = 'Who did Jack Ma hide from?'

# from https://www.bbc.com/news/business-55728338 
news_article_snippet = """ context:
Alibaba Group founder Jack Ma has made his first appearance since Chinese regulators cracked down on his business empire.
His absence had fuelled speculation over his whereabouts amid increasing official scrutiny of his businesses.
The billionaire met 100 rural teachers in China via a video meeting on Wednesday, according to local government media.
Alibaba shares surged 5% on Hong Kong's stock exchange on the news.
"""
# join question with context, works with Pandas DF aswell!
questions = [
             question1+ news_article_snippet,
             question2+ news_article_snippet,
             question3+ news_article_snippet,
             question4+ news_article_snippet,
             question5+ news_article_snippet,
             question6+ news_article_snippet,]
nlp.load('answer_question').predict(questions)

nlp.load('en.t5').predict('Who is president of Nigeria?')
>>> Muhammadu Buhari 

nlp.load('en.t5').predict('What is the most spoken language in India?')
>>> Hindi

nlp.load('en.t5').predict('What is the capital of Germany?')
>>> Berlin