Search results
Results From The WOW.Com Content Network
At Salesforce, we build an AI coding assistant demo using CodeT5 as a VS Code plugin to provide three capabilities: Text-to-code generation: generate code based on the natural language description.
CodeT5 achieves state-of-the-art performance on multiple code-related downstream tasks including understanding tasks such as code defect detection and clone detection, and generation tasks across various directions including PL-NL, NL-PL, and PL-PL. In what follows, we will explain how CodeT5 works.
"We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers.
We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers.
CodeT5+ is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. encoder-only, decoder-only, and encoder-decoder) to support a wide range of code understanding and generation tasks.
CodeT5 is a new model that uses Transformer technology to better understand and generate code. It is based on the T5 architecture, which is a neural language model with a sequence-to-sequence (Seq2Seq) architecture.
CodeT5+ is a new family of open code LLMs trained with flexible model architecture and diverse learning objectives. Operating as encoder-only, decoder-only, or encoder-decoder models, CodeT5+ can be easily adapted to many downstream tasks, including both code understanding and generation tasks.
We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers.
In this section, we compare CodeT5 with SOTA models on a broad set of CodeXGLUE downstream tasks (§5.1), and investigate the effects of our bimodal dual generation and multi-task learning (§5.2), followed by a detailed analysis on the pro-posed identifier-aware pre-training (§5.3).
CodeT5 is a Transformer-based model for code understanding and generation based on the T5 architecture. It utilizes an identifier-aware pre-training objective that considers the crucial token type information (identifiers) from code.