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PDF	CodeSearchNet Challenge Evaluating the State of Semantic Code 8 June 2020 the CodeSearchNet Corpus and are presenting the CodeSearch-. Net Challenge which consists of 99 natural language queries with.

PDF	Learning Deep Semantic Model for Code Search using 28 Jan 2022 the CodeSearchNet challenge [1] about semantic code search. We align cross-lingual embed- ding for multi-modality learning with large ...

PDF	PyTorrent: A Python Library Corpus for Large-scale Language Models 4 Oct 2021 Firstly Unlike CodeSearchNet

PDF	Multimodal Representation for Neural Code Search

PDF	Bag-of-Words Baselines for Semantic Code Search 1 Aug 2021 RM3 on the CodeSearchNet Corpus

PDF	AugmentedCode: Examining the Effects of Natural Language 16 Oct 2021 gramming language which outperforms on CodeSearchNet and CodeBERT with a Mean Reciprocal Rank (MRR) of 0.73 and 0.96 respectively.

PDF	Code Summarization: Do Transformers Really Understand Code? our analysis on CodeSearchNet TL-CodeSum and. 108. Python datasets for Python and Java programming. 109 languages. The data statistics are provided in Ap-.

PDF	GraphSearchNet: Enhancing GNNs via Capturing Global 9 Feb 2022 The extensive experiments on Java and Python programming language from the public benchmark CodeSearchNet confirm that. GraphSearchNet ...

PDF	CoDesc: A Large Code–Description Parallel Dataset 1 Aug 2021 Code Search and Summarization CODE-NN. (Iyer et al. 2016) is a pioneering work in data- driven code summarization. The CodeSearchNet dataset ...

PDF	Automatic Code Generation using Pre-Trained Language Models Figure 4 explains the general architecture of the baseline models from the CodeSearchNet task. We successfully trained and evaluated two baselines: Neural-Bag-

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PDF	Report - CS230 Deep Learning - Stanford University Baselines from the CodeSearchNet models for code search were trained and tuned by Luis Perez and Sudhardshan Viswanathan Data analysis and

PDF	Semantic Code Search with Neural Bag-of-Words and Graph CodeSearchNet Challenge Our Neural Bag-of-Words encoder improves the MRR by 4 38 for Python and 4 98 for Java The Graph Convolutional Network

PDF	Semantic code search using Code2Vec: A bag-of-paths - UWSpace Migrating CodeSearchNet [19] benchmark neural models in Tensorflow 2 0 • Implementing Code2Vec [3] into CodeSearchNet and evaluating its performance

PDF	A Pre-Trained Model for Programming and Natural Languages 16 nov 2020 · related code from a collection of codes We con- duct experiments on the CodeSearchNet corpus (Husain et al , 2019) 4 We follow the official

PDF	Multi-modal Neural Representations for Semantic Code Search - DiVA These baseline models prepared by the CodeSearchNet Challenge are rather strong, therefore, we would investigate how well these baseline models perform

PDF	Semantic Robustness of Models of Source Code - Computer (i) code2seq's java-small dataset (c2s/java-small) (Alon et al , 2018), (ii) GitHub's CodeSearchNet Java and Python datasets (csn/java, csn/python) (Husain et al ,

PDF	Survey of Embedding Performance for Semantic Code - Sean Kelley In their paper introducing the CodeSearchNet challenge, the GitHub team defined their baseline neural model architecture which relies heavily on embeddings

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PDF] CodeSearchNet Challenge: Evaluating the State of Semantic — PDF] CodeSearchNet Challenge: Evaluating the State of Semantic

PDF] CodeSearchNet Challenge: Evaluating the State of Semantic — PDF] CodeSearchNet Challenge: Evaluating the State of Semantic

PDF] CodeSearchNet Challenge: Evaluating the State of Semantic — PDF] CodeSearchNet Challenge: Evaluating the State of Semantic

PDF] CodeSearchNet Challenge: Evaluating the State of Semantic — PDF] CodeSearchNet Challenge: Evaluating the State of Semantic

PDF] CodeSearchNet Challenge: Evaluating the State of Semantic — PDF] CodeSearchNet Challenge: Evaluating the State of Semantic

PDF] CodeSearchNet Challenge: Evaluating the State of Semantic — PDF] CodeSearchNet Challenge: Evaluating the State of Semantic

JuICe Dataset — JuICe Dataset

GitHub Releases Dataset of Six Million Open-Source Methods for — GitHub Releases Dataset of Six Million Open-Source Methods for

Contrastive Code Representation Learning — Contrastive Code Representation Learning

GraphCodeBERT: Pre-training Code Representations with Data Flow — GraphCodeBERT: Pre-training Code Representations with Data Flow

PDF) The Effectiveness of Pre-Trained Code Embeddings — PDF) The Effectiveness of Pre-Trained Code Embeddings

Arxiv Sanity Preserver — Arxiv Sanity Preserver

GraphCodeBERT: Pre-training Code Representations with Data Flow — GraphCodeBERT: Pre-training Code Representations with Data Flow

GitHub Releases Dataset of Six Million Open-Source Methods for — GitHub Releases Dataset of Six Million Open-Source Methods for

Arxiv Sanity Preserver — Arxiv Sanity Preserver

PDF) Contrastive Code Representation Learning — PDF) Contrastive Code Representation Learning

Optimizing Leak Detection in Open-source Platforms with Machine — Optimizing Leak Detection in Open-source Platforms with Machine

GitHub Releases Dataset of Six Million Open-Source Methods for — GitHub Releases Dataset of Six Million Open-Source Methods for

Optimizing Leak Detection in Open-source Platforms with Machine — Optimizing Leak Detection in Open-source Platforms with Machine

Machine_Learning_and_Artificial_Intelligence_Projects_1571040805 — Machine_Learning_and_Artificial_Intelligence_Projects_1571040805

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Open Graph Benchmark: Datasets for Machine Learning on Graphs — Open Graph Benchmark: Datasets for Machine Learning on Graphs

Arxiv Sanity Preserver — Arxiv Sanity Preserver

Gradient Dissent - A Machine Learning Podcast by W\u0026B – Lyssna här — Gradient Dissent - A Machine Learning Podcast by W\u0026B – Lyssna här

When Deep Learning Met Code Search — When Deep Learning Met Code Search

GraphCodeBERT: Pre-training Code Representations with Data Flow — GraphCodeBERT: Pre-training Code Representations with Data Flow

Optimizing Leak Detection in Open-source Platforms with Machine — Optimizing Leak Detection in Open-source Platforms with Machine

PDF) CodeBERT: A Pre-Trained Model for Programming and Natural — PDF) CodeBERT: A Pre-Trained Model for Programming and Natural

Arxiv Sanity Preserver — Arxiv Sanity Preserver

GitHub - UmarFarooqui/MethodNamePrediction: Predicting method — GitHub - UmarFarooqui/MethodNamePrediction: Predicting method

Applications of AI in classical software engineering — Applications of AI in classical software engineering

Arxiv Sanity Preserver — Arxiv Sanity Preserver

Arxiv Sanity Preserver — Arxiv Sanity Preserver

Language-Agnostic Representation Learning of Source Code from — Language-Agnostic Representation Learning of Source Code from

PyMT5: multi-mode translation of natural language and Python code — PyMT5: multi-mode translation of natural language and Python code

PDF) NaturalCC: A Toolkit to Naturalize the Source Code Corpus — PDF) NaturalCC: A Toolkit to Naturalize the Source Code Corpus

AI

PDF) A parallel corpus of Python functions and documentation — PDF) A parallel corpus of Python functions and documentation

PDF) Open Graph Benchmark: Datasets for Machine Learning on Graphs — PDF) Open Graph Benchmark: Datasets for Machine Learning on Graphs

Generating Question Titles for Stack Overflow from Mined Code — Generating Question Titles for Stack Overflow from Mined Code

CodeBERT: A Pre-Trained Model for Programming and Natural Languages — CodeBERT: A Pre-Trained Model for Programming and Natural Languages

GraphCodeBERT: Pre-training Code Representations with Data Flow — GraphCodeBERT: Pre-training Code Representations with Data Flow

PDF) Natural Language Processing Techniques - basic concepts and — PDF) Natural Language Processing Techniques - basic concepts and

TelematikaORG — TelematikaORG

CodeBERT: A Pre-Trained Model for Programming and Natural Languages — CodeBERT: A Pre-Trained Model for Programming and Natural Languages

PDF) Traceability Transformed: Generating moreAccurate Links with — PDF) Traceability Transformed: Generating moreAccurate Links with

PDF) Automated Source Code Generation and Auto-Completion Using — PDF) Automated Source Code Generation and Auto-Completion Using

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