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Difference between language model and text classification

Hi All, Can anyone help to elaborate what is the difference between language model and text classification? is text classification only one application of the language model?

And in the course, it says “For a seq2seq model. The input task is very common in NLP (e.g. text classification), and doesn’t require any processing of the input sequence. The output task is equivalent to the task of language modeling, so it requires processing of the output sequence.” What is the “processing” here?


Course: Educative: Interactive Courses for Software Developers
Lesson: Educative: Interactive Courses for Software Developers

Hi @Kk_l

The language model is a probability distribution over sequences of words, while the text classification is a method involved with classifying text into organized groups. Text classification is an application of language modeling.

No text classification is not the only application of the language model, machine translation, part-of-speech tagging, handwriting recognition, and many other application of the language model there.

“Processing” means we do not have to clean the data or perform any pre-processing on input data.