docstrings in python programming

What is docstrings?

To quote PEP 257 from python's own documentation,

A docstring is a string literal that occurs as the first statement in a module, function, class, or method definition. Such a docstring becomes the __doc__ special attribute of that object.

To ease this definition out, basically, a docstring is a piece of string or a small note which is generally provided in a class, function or a method definition for informing as well as documenting important information about the class, function or the method.

There are two basic types of docstrings, which are oneliner docstrings and multiline docstrings.

How to write docstrings?

docstrings are generally literal strings and therefore you can write them within """ triple double quotes""". But there are certain rules in case of unicode or backslashes.

If your docstring information is supposed to have backslash character then you have to use r"""raw triple double quotes""". If you have unicode docstring, then you have to use u""" unicoded triple double quote""".

What to write in docstrings?

There are a few guidelines for writing docstrings.

(1) docstrings should be written in triple quotes whether it is a one liner or not. Because it helps to maintain the structure and expand the doc later on if needed.

(2) docstrings for functions and methods should be in the form of instructions and not descriptions; i.e. instead of writing "it returns sum of two digits" one should write "return sum of two digits".

(3) docstring should be a proper summary of the object you are writing it for, instead of having any coding style writing like "func(a,b) -> a+b". It is supposed to give the reader or user a proper understanding of the main idea and the tasks performed by the object; which is not visible from the raw code.

(4) For a class or a module, you are expected to write long docstrings, containing small summaries of each of the class, function and methods involved. In case of a script, the docstring should be invoked when a wrong argument is passed or a help option is invoked. These are some advanced ideas in terms of docstrings.

How to read docstrings?

A docstring is a basic attribute of any python object. If there is a documentation provided in the object's source code, then object.__doc__ will provide the documentation. i.e. in order to access the docstring of a object, you have to write

print(object.__doc__) and then if it is present then it will get printed. If there is no docstring written then it will return nothing.

Conclusion:

In conclusion, I will advise you to start writing docstrings for your modules and functions from today. If you are writing a code which is to be reused then it is your duty to make it commented and well documented so that both users and developers who are going to either use it or work on further versions of it ; do not face steep problems in terms of readability and understanding of the code because of lack of maintenance. So start writing those docstrings from today.

Tinder bio generation with OpenAI GPT-3 API

Introduction: Recently I got access to OpenAI API beta. After a few simple experiments, I set on creating a simple test project. In this project, I will try to create good tinder bio for a specific person. The abc of openai API playground: In the OpenAI API playground, you get a prompt, and then you can write instructions or specific text to trigger a response from the gpt-3 models. There are also a number of preset templates which loads a specific kind of prompt and let's you generate pre-prepared results. What are the models available? There are 4 models which are stable. These are: (1) curie (2) babbage (3) ada (4) da-vinci da-vinci is the strongest of them all and can perform all downstream tasks which other models can do. There are 2 other new models which openai introduced this year (2021) named da-vinci-instruct-beta and curie-instruct-beta. These instruction models are specifically built for taking in instructions. As OpenAI blog explains and also you will see in our

Can we write codes automatically with GPT-3?

Introduction: OpenAI created and released the first versions of GPT-3 back in 2021 beginning. We wrote a few text generation articles that time and tested how to create tinder bio using GPT-3 . If you are interested to know more on what is GPT-3 or what is openai, how the server look, then read the tinder bio article. In this article, we will explore Code generation with OpenAI models. It has been noted already in multiple blogs and exploration work, that GPT-3 can even solve leetcode problems. We will try to explore how good the OpenAI model can "code" and whether prompt tuning will improve or change those performances. Basic coding: We will try to see a few data structure coding performance by GPT-3. (a) Merge sort with python: First with 200 words limit, it couldn't complete the Write sample code for merge sort in python. def merge(arr, l, m, r): n1 = m - l + 1 n2 = r- m # create temp arrays L = [0] * (n1) R = [0] * (n

What is Bort?

Introduction: Bort, is the new and more optimized version of BERT; which came out this october from amazon science. I came to know about it today while parsing amazon science's news on facebook about bort. So Bort is the newest addition to the long list of great LM models with extra-ordinary achievements. Why is Bort important? Bort, is a model of 5.5% effective and 16% total size of the original BERT model; and is 20x faster than BERT, while being able to surpass the BERT model in 20 out of 23 tasks; to quote the abstract of the paper, ' it obtains performance improvements of between 0 . 3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks. ' So what made this achievement possible? The main idea behind creation of Bort is to go beyond the shallow depth of weight pruning, connection deletion or merely factoring the NN into different matrix factorizations and thus distilling it. While methods like knowle

Machine learning and statistics with python

Search This Blog