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/* | |
* | |
* Проект: Библиотека (книга) | |
* | |
* | |
TODO: Создайте структуру book со следующими полями: | |
Назначение поля | |
фамилия автора | |
имя автора |
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#include <iostream> | |
#include <math.h> | |
#define P 3.14 | |
using namespace std; | |
int main() { | |
int a = 0, b = 0, c = 0, d = 0, r = 0, d1 = 0, p1 = 0, s1 = 0, p2 = 0, s2 = 0, s3 = 0, s4 = 0, g = 0, h = 0, k = 0; | |
double p = 0, s = 0; | |
cout << " menu \n"; |
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<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="UTF-8"> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<meta http-equiv="X-UA-Compatible" content="ie=edge"> | |
<title>Document</title> | |
</head> | |
<body> | |
<div class="middle"> |
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<!-- You can write hire ehat you want --> | |
<div class="text">Click me!</div> |
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<div class="slide s1"><img src="http://blog.domainparking.ru/wp-content/uploads/2019/04/dotmusic-1024x683.jpeg" alt=""></div> | |
<div class="slide s2"><img src="https://i.artfile.me/wallpaper/10-04-2018/5184x3456/muzyka--drugoe-ruki-1326150.jpg" alt=""></div> | |
<div class="slide s1"><img src="https://images.wallpaperscraft.ru/image/dj_naushniki_ustanovka_122020_4135x2756.jpg" alt=""></div> | |
<div class="slide s2"><img src="https://vibirai.ru/image/1259687.jpg" alt=""></div> | |
<div class="slide s1"><img src="https://wallbox.ru/wallpapers/main/201134/gitara-muzyka-siniy-e56f2fa.jpg" alt=""></div> |
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#include <bits/stdc++.h> | |
using namespace std; | |
#define INF INT_MAX | |
#define INFLL LONG_LONG_MAX | |
#define F first | |
#define S second | |
#define pb push_back | |
#define all(a) a.begin(),a.end() | |
#define rep(i,a,b) for(auto i=(a); i<(b); i++) | |
#define dec(i,n,a) for(auto i=(n); i>=(a); i--) |
This approach involves the creation of a dataset comprising pairs of queries and their relevant chunks (code snippets or documentation), which serve as the foundation for training a Cross-Encoder model like BERT for relevance scoring. The aim is to enhance the accuracy of code generation tasks by leveraging these relevance scores.
- Data Preparation: Construct pairs of queries and corresponding relevant chunks. These chunks can be code snippets, documentation excerpts, or any other relevant textual content.
- Dataset Role: These pairs form the dataset on which the Cross-Encoder model will be trained. The dataset is designed to capture the relevance of each chunk to its associated query.