Natural Language Processing (1. Transformer Models)
What is NLP?
NLP is a field of linguistics and machine learning focused on understanding everything related to human language. The aim of NLP tasks is not only to understand single words individually, but to be able to understand the context of those words.
Common NLP tasks are:
Classifying whole sentences: Getting the sentiment of a review, detecting if an email is spam, determining if a sentence is grammatically correct or whether two sentences are logically related or not.
Classifying each word in a sentence: Identifying the grammatical components of a sentence(noun, verb, adjective), or the named entities(person, location, organization)
Generating text content: Completing a prompt with auto-generated text, filling in the blanks in a text with masked words
Extracting an answer from a text: Given a question and a context, extracting the answer to the question based on the information provided in the context
Generating a new sentence from an input text: Translating a text into another language, summarizing a text.
NLP isn’t limited to written text though. It also tackles complex challenges in speech recognition and computer vision, such as generating a transcript of an audio sample or a description of an image.
Transformers, what can they do?
link: https://huggingface.co/learn/nlp-course/chapter1/3?fw=pt
Summary of the content:
- Transformers library
- Working with pipelines
Working with pipelines
The pipeline function returns an end-to-end object that performs an NLP task on one or several texts.