GPT-2 Model for Leetcode Questions in python

Overview

Leetcode using AI 🤖

GPT-2 Model for Leetcode Questions in python New demo here: https://huggingface.co/spaces/gagan3012/project-code-py

Note: the Answers might not make sense in some cases because of the bias in GPT-2 Current accuracy is capped at 90%.

Contribtuions: If you would like to make the model/UI better contributions (Issues/PRs) are welcome Check out CONTRIBUTIONS

How I built this : Linkedin

📢 Favour:

It would be highly motivating, if you can STAR this repo if you find it helpful. New improvements incoming!

Model

Two models have been developed for different use cases and they can be found at https://huggingface.co/gagan3012

The model weights can be found here: GPT-2 and DistilGPT-2

The model has been trained using Weights and Biases (Wandb) and PyTorch

GPT Neo model: https://huggingface.co/gagan3012/project-code-py-neo

Example usage:

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("gagan3012/project-code-py")

model = AutoModelWithLMHead.from_pretrained("gagan3012/project-code-py")

Demo

Streamlit App

A streamlit webapp has been setup to use the model: https://share.streamlit.io/gagan3012/project-code-py/app.py

image

Please create an issue in this repo if the demo is not working

Example results:

Question:

Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.

Answer:

2->3 b = 3->1->2 t = ListNode(-1, 1) Note: The lexicographic ordering of the nodes in a tree matters. Do not assign values to nodes in a tree. Example 1: Input: [1,2,3] Output: 1->2->5 Explanation: 1->2->3->3->4, then 1->2->5[2] and then 5->1->3->4. Note: The length of a linked list will be in the range [1, 1000]. Node.val must be a valid LinkedListNode type. Both the length and the value of the nodes in a linked list will be in the range [-1000, 1000]. All nodes are distinct. """ # Definition for singly-linked list. # class ListNode: # def __init__(self, x): # self.val = x # self.next = None class Solution: def deleteNode(self, head: ListNode, val: int) -> None: """ BFS Linked List :param head: ListNode :param val: int :return: ListNode """ if head is not None: return head dummy = ListNode(-1, 1) dummy.next = head dummy.next.val = val dummy.next.next = head dummy.val = "" s1 = Solution() print(s1.deleteNode(head)) print(s1.deleteNode(-1)) print(s1.deleteNode(-1)) ">
""" Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list.

For example,
a = 1->2->3
b = 3->1->2
t = ListNode(-1, 1)

Note: The lexicographic ordering of the nodes in a tree matters. Do not assign values to nodes in a tree.
Example 1:

Input: [1,2,3]
Output: 1->2->5
Explanation: 1->2->3->3->4, then 1->2->5[2] and then 5->1->3->4.


Note:

The length of a linked list will be in the range [1, 1000].
Node.val must be a valid LinkedListNode type.
Both the length and the value of the nodes in a linked list will be in the range [-1000, 1000].
All nodes are distinct.
"""
# Definition for singly-linked list.
# class ListNode:
#     def __init__(self, x):
#         self.val = x
#         self.next = None

class Solution:
    def deleteNode(self, head: ListNode, val: int) -> None:
        """
        BFS
        Linked List
        :param head: ListNode
        :param val: int
        :return: ListNode
        """
        if head is not None:
            return head
        dummy = ListNode(-1, 1)
        dummy.next = head
        dummy.next.val = val
        dummy.next.next = head
        dummy.val = ""


s1 = Solution()
print(s1.deleteNode(head))
print(s1.deleteNode(-1))
print(s1.deleteNode(-1))
Owner
Gagan Bhatia
Software Developer | Machine Learning Enthusiast
Gagan Bhatia
A library for finding knowledge neurons in pretrained transformer models.

knowledge-neurons An open source repository replicating the 2021 paper Knowledge Neurons in Pretrained Transformers by Dai et al., and extending the t

EleutherAI 96 Dec 21, 2022
This project consists of data analysis and data visualization (done using python)of all IPL seasons from 2008 to 2019 and answering the most asked questions about the IPL.

IPL-data-analysis This project consists of data analysis and data visualization of all IPL seasons from 2008 to 2019 and answering the most asked ques

Sivateja A T 2 Feb 08, 2022
Fine-tuning scripts for evaluating transformer-based models on KLEJ benchmark.

The KLEJ Benchmark Baselines The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language und

Allegro Tech 17 Oct 18, 2022
Unofficial Python library for using the Polish Wordnet (plWordNet / Słowosieć)

Polish Wordnet Python library Simple, easy-to-use and reasonably fast library for using the Słowosieć (also known as PlWordNet) - a lexico-semantic da

Max Adamski 12 Dec 23, 2022
A curated list of efficient attention modules

awesome-fast-attention A curated list of efficient attention modules

Sepehr Sameni 891 Dec 22, 2022
Count the frequency of letters or words in a text file and show a graph.

Word Counter By EBUS Coding Club Count the frequency of letters or words in a text file and show a graph. Requirements Python 3.9 or higher matplotlib

EBUS Coding Club 0 Apr 09, 2022
A list of NLP(Natural Language Processing) tutorials built on Tensorflow 2.0.

A list of NLP(Natural Language Processing) tutorials built on Tensorflow 2.0.

Won Joon Yoo 335 Jan 04, 2023
Use PaddlePaddle to reproduce the paper:mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

MT5_paddle Use PaddlePaddle to reproduce the paper:mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer English | 简体中文 mT5: A Massively

2 Oct 17, 2021
Long text token classification using LongFormer

Long text token classification using LongFormer

abhishek thakur 161 Aug 07, 2022
Module for automatic summarization of text documents and HTML pages.

Automatic text summarizer Simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains sim

Mišo Belica 3k Jan 08, 2023
Big Bird: Transformers for Longer Sequences

BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the c

Google Research 457 Dec 23, 2022
Tensorflow Implementation of A Generative Flow for Text-to-Speech via Monotonic Alignment Search

Tensorflow Implementation of A Generative Flow for Text-to-Speech via Monotonic Alignment Search

Ankur Dhuriya 10 Oct 13, 2022
A paper list for aspect based sentiment analysis.

Aspect-Based-Sentiment-Analysis A paper list for aspect based sentiment analysis. Survey [IEEE-TAC-20]: Issues and Challenges of Aspect-based Sentimen

jiangqn 419 Dec 20, 2022
FB ID CLONER WUTHOT CHECKPOINT, FACEBOOK ID CLONE FROM FILE

* MY SOCIAL MEDIA : Programming And Memes Want to contact Mr. Error ? CONTACT : [ema

Mr. Error 9 Jun 17, 2021
Transformer Based Korean Sentence Spacing Corrector

TKOrrector Transformer Based Korean Sentence Spacing Corrector License Summary This solution is made available under Apache 2 license. See the LICENSE

Paul Hyung Yuel Kim 3 Apr 18, 2022
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

GenSen Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning Sandeep Subramanian, Adam Trischler, Yoshua B

Maluuba Inc. 309 Oct 19, 2022
AI_Assistant - This is a Python based Voice Assistant.

This is a Python based Voice Assistant. This was programmed to increase my understanding of python and also how the in-general Voice Assistants work.

1 Jan 06, 2022
Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5

NLP-Summarizer Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5 This project aimed to provide in

Samuel Sharkey 1 Feb 07, 2022
Higher quality textures for the Metal Gear Solid series.

Metal Gear Solid: HD Textures Higher quality textures for the Metal Gear Solid series. The goal is to maximize the quality of assets that the engine w

Samantha 6 Dec 06, 2022
Data and evaluation code for the paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (EMNLP 2021).

Data and evaluation code for the paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. @inproceedings{tedes

Babelscape 40 Dec 11, 2022