Subtext 0.0.2

A package to make NLP easy, fast, and fun

Subtext 0.0.2

A package to make NLP fast and easy for beginners.

• Efficient text prediction
• Text pairing, equivalent to that of NLTK's n-gram.
• Syllable Identification
• Find frequencies of words in given text
• Find matching words in two arrays

I still have a lot of plans for this package, for that reason, there would be a lot of frequent updates in the near future. The updates would include optimizations & more functions, so stay tuned.

Install

pip install subtext

Usage

First import the program using:

import subtext

Predict

A function that predicts the next x number of words based on the given string and phrase

Parameters

The function's parameters are:

subtext.predict(string, phrase, n=0, case_insensitive=False)

• String: Main text
• Phrase: The key phrase (prompt). The function would try to predict what would come after the given phrase.
• n: The number of words it would return. It's automomatically set to 0, which would return all predictions regardless of their corresponding word counts.
• case_insensitive: Set this to True if you want to.

Actual usage

So, let's try to use this.

string="I am a string. I am also a human being, but most importantly, I am a string."
print(predict(string, "I am", n=1))

This would output

{'a': 2, 'also': 1}

But, if you change the n value,

print(predict(string, "I am", n=2))

It would output

{'a string.': 2, 'also a': 1}

Pair

This function splits a string into pairs of strings.

Parameters

subtext.pair(string, n)

• string is the string you're trying to split into pairs
• n stands for the number of strings in each pair. (Equivalent to that of the n value in n-gram)

Usage

Let's set our string to:

string="Sometimes, I just go out and eat sand. I don't know why"

Don't ask. Let's turn this into pairs of 2:

print(pair(string, 2))

Which outputs

[['Sometimes,', 'I'], ['I', 'just'], ['just', 'go'], ['go', 'out'], ['out', 'and'], ['and', 'eat'], ['eat', 'sand.'], ['sand.', 'I'], ['I', "don't"], ["don't", 'know'], ['know', 'why']]

Identify Syllables

subtext.syllables("carbonmonoxide")

This outputs:

car-bon-mon-ox-ide

But take note that this only works with lowercase strings.

Countwords

Parameters

The function's parameters are:

subtext.countwords(string, case_insensitive=False)

Change that to True if you want it to be case-insensitive.

Actual usage

Get yourself a nice string

string = "Sometimes I wonder, 'Am I stupid?' then I realize, yeah. yeah, I am stupid."

Then put it in the function:

x = subtext.countwords(string)
print(x)

It should print:

{'I': 4, 'Sometimes': 1, 'wonder,': 1, "'Am": 1, "stupid?'": 1, 'then': 1, 'realize,': 1, 'yeah.': 1, 'yeah,': 1, 'am': 1, 'stupid.': 1}

Matchingwords

A function that finds & counts matching words in two strings

Actual usage

So in this case, our strings are:

string1, string2 = "God, I love drawing, drawing is my favourite thing to do", "God, I hate drawing, drawing is my least favourite thing to do"

If we run this through matchingwords, we would get:

{'God,': 1, 'I': 1, 'drawing,': 1, 'drawing': 1, 'is': 1, 'my': 1, 'favourite': 1, 'thing': 1, 'to': 1, 'do': 1}