This repository contains an implementation of a Bigram Model in Python. There are approx 40,000 names in the names.txt file which is used to generate more names using the bigram concept.
A Bigram Model is a statistical language model that is based on the probability of a word given its preceding word. It's a simple but effective way to model the probability of a sequence of words in a text. This implementation provides a straightforward Python code to build and use a Bigram Model for text generation or other natural language processing tasks.
A Bigram Model is a type of probabilistic model that predicts the likelihood of a word given the previous word in a sequence of words. In other words, it models the conditional probability of observing a word given the previous word. The "bi" in "bigram" refers to the fact that it considers pairs of consecutive words.
For example, consider the sentence: "Chatbots are amazing". In terms of bigrams, this sentence can be represented as:
- (START, Chatbots)
- (Chatbots, are)
- (are, amazing)
- (amazing, END)
The Bigram Model calculates the probability of a word given the previous word using the following formula:
P(word | previous_word) = Count(previous_word, word) / Count(previous_word)
Where:
Count(previous_word, word)is the number of times the word word follows the word previous_word.Count(previous_word)is the number of times the word previous_word occurs in the dataset.- The model uses these probabilities to generate text or make predictions based on a given context.