A Comprehensive Guide to Build your own Language Model in Python!

Overview Language models are a crucial component in the Natural Language Processing (NLP) journey These language models power all the popular NLP applications we are familiar with – Google Assistant, Siri, Amazon’s Alexa, etc.

We will go from basic language models to advanced ones in Python here   Introduction “We tend to look through language and not realize how much power language has.

” Language is such a powerful medium of communication.

We have the ability to build projects from scratch using the nuances of language.

It’s what drew me to Natural Language Processing (NLP) in the first place.

I’m amazed by the vast array of tasks I can perform with NLP – text summarization, generating completely new pieces of text, predicting what word comes next (Google’s autofill), among others.

Do you know what is common among all these NLP tasks?.They are all powered by language models!.Honestly, these language models are a crucial first step for most of the advanced NLP tasks.

In this article, we will cover the length and breadth of language models.

We will begin from basic language models that can be created with a few lines of Python code and move to the State-of-the-Art language models that are trained using humongous data and are being currently used by the likes of Google, Amazon, and Facebook, among others.

So, tighten your seatbelts and brush up your linguistic skills – we are heading into the wonderful world of Natural Language Processing!.Are you new to NLP?.Confused about where to begin?.You should check out this comprehensive course designed by experts with decades of industry experience: Natural Language Processing (NLP) with Python   Table of Contents What is a Language Model in NLP?.Building an N-gram Language Model Building a Neural Language Model Natural Language Generation using OpenAI’s GPT-2   What is a Language Model in NLP?.“You shall know the nature of a word by the company it keeps.

” – John Rupert Firth A language model learns to predict the probability of a sequence of words.

But why do we need to learn the probability of words?.Let’s understand that with an example.

I’m sure you have used Google Translate at some point.

We all use it to translate one language to another for varying reasons.

This is an example of a popular NLP application called Machine Translation.

In Machine Translation, you take in a bunch of words from a language and convert these words into another language.

Now, there can be many potential translations that a system might give you and you will want to compute the probability of each of these translations to understand which one is the most accurate.

In the above example, we know that the probability of the first sentence will be more than the second, right?.That’s how we arrive at the right translation.

This ability to model the rules of a language as a probability gives great power for NLP related tasks.

Language models are used in speech recognition, machine translation, part-of-speech tagging, parsing, Optical Character Recognition, handwriting recognition, information retrieval, and many other daily tasks.

  Types of Language Models There are primarily two types of Language Models: Statistical Language Models: These models use traditional statistical techniques like N-grams, Hidden Markov Models (HMM) and certain linguistic rules to learn the probability distribution of words Neural Language Models: These are new players in the NLP town and have surpassed the statistical language models in their effectiveness.

They use different kinds of Neural Networks to model language Now that you have a pretty good idea about Language Models, let’s start building one!.  Building an N-gram Language Model What are N-grams (unigram, bigram, trigrams)?.An N-gram is a sequence of N tokens (or words).

Let’s understand N-gram with an example.

Consider the following sentence: “I love reading blogs about data science on Analytics Vidhya.

” A 1-gram (or unigram) is a one-word sequence.

For the above sentence, the unigrams would simply be: “I”,  “love”, “reading”, “blogs”,  “about”, “data”, “science”, “on”, “Analytics”, “Vidhya”.

A 2-gram (or bigram) is a two-word sequence of words, like “I love”, “love reading”, or “Analytics Vidhya”.

And a 3-gram (or trigram) is a three-word sequence of words like “I love reading”, “about data science” or “on Analytics Vidhya”.

Fairly straightforward stuff!. More details

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