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相关概念视频

Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Code4ML:一个大规模的数据集,包含注释的机器学习代码.

Anastasia Drozdova1, Ekaterina Trofimova1, Polina Guseva1

  • 1Department of Computer Science, NRU Higher School of Economics, Moscow, Russia.

PeerJ. Computer science
|June 22, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了Code4ML集体,这是一个来自Kaggle的注释机器学习 (ML) 代码片段的大数据集. 该资源通过为分类和生成等任务提供标记代码来帮助ML开发.

关键词:
Jupyter 的代码片段ML 代码数据集的数据集

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科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 软件工程 软件工程 软件工程
  • 数据科学数据科学数据科学

背景情况:

  • 程序代码越来越多地被用作数据科学中的数据源,用于语义分类和程序生成等任务.
  • 机器学习模型的应用受到注释代码片段数据集的缺乏所阻碍.

研究的目的:

  • 为了解决机器学习的注释代码数据集的稀缺性.
  • 介绍Code4ML集体,这是一个全面的注释ML代码片段集.

主要方法:

  • 从Kaggle上托管的10万个Jupyter笔记本中收集了大约250万个机器学习代码片段.
  • 使用定制设计的,用户友好的界面,注释了这些代码片段的代表部分.
  • 包括相关的元数据,如任务总结,竞赛细节和数据集描述.

主要成果:

  • Code4ML集体提供了一个大规模的,注释的ML代码片段数据集.
  • 数据集来自Kaggle的公开数据,Kaggle是一个领先的数据科学竞争平台.
  • 在收集的代码片段的很大一部分上进行了人类注释.

结论:

  • Code4ML数据集为数据科学和软件工程研究提供了宝贵的资源.
  • 它可以促进数据驱动的方法来应对诸如语义代码分类,代码自动完成和基于自然语言的ML任务代码生成等挑战.