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
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.09

Keon Lee 142 Jan 06, 2023
ConferencingSpeech2022; Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge

ConferencingSpeech 2022 challenge This repository contains the datasets list and scripts required for the ConferencingSpeech 2022 challenge. For more

21 Dec 02, 2022
The training code for the 4th place model at MDX 2021 leaderboard A.

The training code for the 4th place model at MDX 2021 leaderboard A.

Chin-Yun Yu 32 Dec 18, 2022
A framework for cleaning Chinese dialog data

A framework for cleaning Chinese dialog data

Yida 136 Dec 20, 2022
Mlcode - Continuous ML API Integrations

mlcode Basic APIs for ML applications. Django REST Application Contains REST API

Sujith S 1 Jan 01, 2022
Production First and Production Ready End-to-End Keyword Spotting Toolkit

Production First and Production Ready End-to-End Keyword Spotting Toolkit

223 Jan 02, 2023
【原神】自动演奏风物之诗琴的程序

疯物之诗琴 读取midi并自动演奏原神风物之诗琴。 可以自定义配置文件自动调整音符来适配风物之诗琴。 (原神1.4直播那天就开始做了!到现在才能放出来。。) 如何使用 在Release页面中下载打包好的程序和midi压缩包并解压。 双击运行“疯物之诗琴.exe”。 在原神中打开风物之诗琴,软件内输入

435 Jan 04, 2023
🤖 Basic Financial Chatbot with handoff ability built with Rasa

Financial Services Example Bot This is an example chatbot demonstrating how to build AI assistants for financial services and banking with Rasa. It in

Mohammad Javad Hossieni 4 Aug 10, 2022
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel

537 Jan 05, 2023
This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest

Rachford-Rice Contest This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest. Can you solve the Rachford-Rice problem for all t

13 Sep 20, 2022
Yet Another Neural Machine Translation Toolkit

YANMTT YANMTT is short for Yet Another Neural Machine Translation Toolkit. For a backstory how I ended up creating this toolkit scroll to the bottom o

Raj Dabre 121 Jan 05, 2023
🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy This package provides spaCy components and architectures to use tr

Explosion 1.2k Jan 08, 2023
An implementation of WaveNet with fast generation

pytorch-wavenet This is an implementation of the WaveNet architecture, as described in the original paper. Features Automatic creation of a dataset (t

Vincent Herrmann 858 Dec 27, 2022
Beyond Paragraphs: NLP for Long Sequences

Beyond Paragraphs: NLP for Long Sequences

AI2 338 Dec 02, 2022
Sample data associated with the Aurora-BP study

The Aurora-BP Study and Dataset This repository contains sample code, sample data, and explanatory information for working with the Aurora-BP dataset

Microsoft 16 Dec 12, 2022
Multispeaker & Emotional TTS based on Tacotron 2 and Waveglow

This Repository contains a sample code for Tacotron 2, WaveGlow with multi-speaker, emotion embeddings together with a script for data preprocessing.

Ivan Didur 106 Jan 01, 2023
Uses Google's gTTS module to easily create robo text readin' on command.

Tool to convert text to speech, creating files for later use. TTRS uses Google's gTTS module to easily create robo text readin' on command.

0 Jun 20, 2021
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
open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

7 Nov 02, 2022
Anomaly Detection 이상치 탐지 전처리 모듈

Anomaly Detection 시계열 데이터에 대한 이상치 탐지 1. Kernel Density Estimation을 활용한 이상치 탐지 train_data_path와 test_data_path에 존재하는 시점 정보를 포함하고 있는 csv 형태의 train data와

CLUST-consortium 43 Nov 28, 2022