Sequence generation tasks

Natural Language Processing (NLP) in Python

Fouad Trad

Machine Learning Engineer

Sequence generation

  • Produces new text based on given input
  • Includes tasks like:
    • Text summarization
    • Text translation
    • Language modeling

GIF for a pencil writing sentences on a paper.

Natural Language Processing (NLP) in Python

Text summarization

  • Condenses long documents into shorter versions highlighting key points
  • Useful when dealing with:
    • Lengthy news articles
    • Research papers
    • Reports
    • Emails

Image showing a pile of books and a summary document generated out of them.

Natural Language Processing (NLP) in Python

Text summarization pipeline

from transformers import pipeline

summarizer = pipeline(task="summarization", model="cnicu/t5-small-booksum")
text = """The Amazon rainforest, often referred to as the "lungs of the Earth," is one of the most biologically diverse regions in the world. Spanning over nine countries in South America, the majority of the forest lies in Brazil. It is home to an estimated 390 billion individual trees, divided into 16,000 different species. The rainforest plays a critical role in regulating the global climate by absorbing vast amounts of carbon dioxide and producing oxygen."""
result = summarizer(text)
print(result)
[{'summary_text': 'the Amazon rainforest is one of the most biologically diverse regions in the world. 
The majority of the forest lies in Brazil. The rainforest plays a critical role in regulating the 
global climate by absorbing vast amounts of carbon dioxide and producing oxygen.'}]
Natural Language Processing (NLP) in Python

Text translation

  • Converts text from one language to another
  • Crucial in multilingual applications:
    • International websites
    • Customer support tools

Image showing a text being translated from English (Good morning) to French (Bonjour).

Natural Language Processing (NLP) in Python

Text translation pipeline

translator = pipeline(task="translation", model="Helsinki-NLP/opus-mt-en-fr")

sentence = "The rainforest helps regulate the Earth's climate."
result = translator(sentence)
print(result)
[{'translation_text': 'La forêt tropicale aide à réguler le climat de la Terre.'}]
Natural Language Processing (NLP) in Python

Language modeling

  • Predict the next words based on a given prompt
  • Basis for many applications:
    • Autocompletion
    • Story generation
    • Chatbot replies

Image showing how language modeling works by providing a prompt to the machine and it provides the generated text.

Natural Language Processing (NLP) in Python

Language modeling pipeline

generator = pipeline(task="text-generation", model="distilgpt2")


prompt = "Once upon a time,"
result = generator(prompt, max_length=30, num_return_sequences=3)
print(result)
[{'generated_text': "Once upon a time, my life wasn't so good, I kept my things tidy. The 
                     more time I spend with my children the more ..."}, 
 {'generated_text': 'Once upon a time, we began a process of finding the right answers to 
                     some big questions," said Jim Pelterer, a lecturer at the 
                     University...'}, 
 {'generated_text': 'Once upon a time, a man came along and took in the city, and found out 
                     that a strange woman had just walked in and was dancing about...'}]
Natural Language Processing (NLP) in Python

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Natural Language Processing (NLP) in Python

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