下图时执行结果对比: 对于两种不同的dataset,性能头提高了3倍。而代码生成消耗的时间大概为150s。代码生成消耗的时间和采用的优化选项有很大关系。可以在impala shell中,使用set命令查看优化选项。 下表是更详细的对比: 可以看到,采用代码生成可以减少一半的指令和一半的branch miss。 6 总结 我们在代码生生成上投入...
5. JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation会议:EMNLP 2019.作者:Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer链接:aclweb.org/anthology/D16. Code Generation as a Dual Task of Code Summarization会议:NeurIPS 2019.作者:Bolin Wei, Ge Li, ...
The task involves enhancing the training of target application (e.g. autonomous driving systems) by generating datasets of diverse and critical elements (e.g. traffic scenarios). Traditional methods rely on expensive and limited datasets, which often fai
Benchmarks Add a Result These leaderboards are used to track progress in Video Generation TrendDatasetBest ModelPaperCodeCompare UCF-101 W.A.L.T-XL (class-conditional) See all BAIR Robot Pushing MAGVIT See all Sky Time-lapse StyleSV (256x256) See all UCF-101 16 frames, ...
( pre-train的data来自额外的Source Code和Natural Language Dataset ) Code Retrieval Module 对于分别来自Natural Language和Source Code和两个Sequence Y和X,相似度计算如下 Score(X,Y) = f_{cr}(h^e,h^{'e})\\h^e = tanh(maxpooling(h^e_1,h^e_2,\dots,h^e_m))\\ h^{'e} = tanh(maxpo...
AI Code Generation Tools reviews, comparisons, alternatives and pricing. The best AI Code Generation solutions for small business to enterprises.
1 PipeLine Engine == Vectorized Model (主) + Code Generation Model (辅) 2 Spark Engine = Code Generation Model(主) + Vetorizied Model (辅) PipeLine Engine 是向量化驱动,CodeGen 优化虚函数, Spark Engine 是 CodeGen 驱动, 向量化跟进, 都有向量化的能力,都有Code Genration 的能力 ...
Dive into the HumanEval dataset and the pass@k metric, revolutionizing the evaluation of Large Language Models in code generation tasks...
To address this gap, we introduce GitChameleon, a novel dataset featuring 116 Python code completion tasks, each tied to specific library versions and accompanied by executable unit tests. This dataset is designed to rigorously evaluate the ability of large language models (LL...
@misc{2206.13179, Author = {Yiyang Hao and Ge Li and Yongqiang Liu and Xiaowei Miao and He Zong and Siyuan Jiang and Yang Liu and He Wei}, Title = {AixBench: A Code Generation Benchmark Dataset}, Year = {2022}, Eprint = {arXiv:2206.13179}, } ...