codet5-large
Maintainer: Salesforce
56
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
codet5-large is a large-sized encoder-decoder AI model developed by Salesforce that can be used for a variety of code-related tasks. It was introduced in the paper "CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation" and is part of the CodeT5 family of models.
Compared to the smaller codet5-base and codet5-small models, codet5-large has 770 million parameters, making it a more capable and powerful model. It was pretrained on a large dataset of code from CodeSearchNet across 6 programming languages, allowing it to understand and generate code more effectively than previous models.
The CodeT5+ models, including the codet5p-16b and instructcodet5p-16b checkpoints, are an even more advanced version of the CodeT5 family. These models are pretrained with additional techniques like span denoising, contrastive learning, and instruction tuning to further improve performance on code-related tasks.
Model inputs and outputs
Inputs
- Code snippet: The model takes in a code snippet, which can be in any of the 6 supported programming languages (Python, Java, JavaScript, PHP, Ruby, Go).
Outputs
- Masked token prediction: The model can be used to predict missing tokens in a partially masked code snippet.
- Code generation: The model can also be used to generate new code, given a natural language prompt or partial code snippet.
Capabilities
codet5-large can effectively understand and manipulate code, making it useful for a variety of applications. It can be used for tasks like:
- Code summarization: Generating natural language descriptions of code snippets.
- Code translation: Translating code from one programming language to another.
- Code completion: Suggesting the next few tokens in a partially written code snippet.
- Code refactoring: Automatically improving the style and structure of code.
- Code defect detection: Identifying bugs and issues in code.
The model's strong performance on these tasks is due to its ability to capture the semantic meaning and structure of code, which it learns from the large pretraining dataset.
What can I use it for?
codet5-large and the broader CodeT5 family of models are well-suited for any project or application that involves working with code. This could include:
- Developer tools: Integrating the model into IDEs, code editors, or other tools to assist developers with their daily tasks.
- Automated programming: Using the model to generate or refine code based on high-level requirements or natural language descriptions.
- Code search and recommendation: Building systems that can retrieve relevant code snippets or suggest code examples based on a user's query.
- Code analysis and understanding: Applying the model to tasks like code summarization, defect detection, and clone detection to gain insights about codebases.
By leveraging the capabilities of codet5-large and related models, you can potentially automate and streamline various code-related workflows, boost developer productivity, and create novel applications that combine natural language and code.
Things to try
One interesting aspect of codet5-large is its ability to handle identifiers (variable names, function names, etc.) in a more sophisticated way. The model was pretrained with a novel "identifier-aware" objective, which allows it to better understand the semantic meaning and context of these important code elements.
You could try experimenting with this capability, for example, by prompting the model to generate code that uses meaningful and contextual variable names, or by evaluating its performance on tasks like identifier prediction or recovery. Exploring how the model's identifier-awareness affects its overall code understanding and generation abilities could yield interesting insights.
Another interesting direction would be to investigate the model's cross-language capabilities. Since it was pretrained on code from multiple programming languages, codet5-large may be able to effectively translate code between languages or transfer knowledge from one language to another. Experimenting with cross-language tasks could unlock new use cases for the model.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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codet5p-16b
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codet5p-16b is a new family of open code large language models with an encoder-decoder architecture introduced by Salesforce. It can operate in different modes (encoder-only, decoder-only, and encoder-decoder) to support a wide range of code understanding and generation tasks. Compared to the original CodeT5 family, codet5p-16b is pretrained with a diverse set of tasks including span denoising, causal language modeling, contrastive learning, and text-code matching. It also uses a "shallow encoder and deep decoder" architecture and an efficient pretraining method to scale up the model. Model inputs and outputs Inputs Code snippets or natural language prompts related to programming tasks Outputs Generated code or natural language responses to the input prompts Capabilities codet5p-16b can be used for a variety of code-related tasks such as code generation, code summarization, code translation, and code defect detection. It has shown strong performance on these tasks compared to previous models. The model can also complete partially-generated code given an input prompt. What can I use it for? codet5p-16b can be particularly useful for software development tasks where you need to generate or understand code. For example, you could use it to help with tasks like: Automatically generating code snippets from natural language descriptions Summarizing the functionality of a code block Translating code between programming languages Detecting potential bugs or issues in code The model's versatility in handling both code and natural language makes it a powerful tool for automating and assisting with various programming-related workflows. Things to try One interesting aspect of codet5p-16b is its ability to operate in different modes, allowing it to be used for a wide range of code-related tasks. You could experiment with using the model in encoder-only, decoder-only, and encoder-decoder modes to see how it performs on different types of inputs and outputs. Additionally, you could try fine-tuning the model on specific programming languages or tasks to further improve its performance on your particular use case. The CodeT5 model provides a good starting point for this, as it has been pretrained on a diverse set of programming languages.
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codet5-small
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The codet5-small model is a pre-trained encoder-decoder Transformer model developed by Salesforce that aims to better leverage the code semantics conveyed from developer-assigned identifiers. It was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. This small-sized model is part of the CodeT5 family, which also includes a base-sized and larger CodeT5+ models. The core innovation of CodeT5 is its unified framework that seamlessly supports both code understanding and generation tasks, allowing for multi-task learning. It also employs a novel identifier-aware pre-training task to enable the model to distinguish code tokens that are identifiers and recover them when masked. Additionally, the authors propose to exploit user-written code comments with a bimodal dual generation task for better alignment between natural language and programming language. Model inputs and outputs Inputs Text strings**: The codet5-small model takes plain text as input, which can be a partial code snippet, a natural language description, or a combination of the two. Outputs Text strings**: The model outputs text, which can be a completed code snippet, a natural language description of code, or a translation between programming languages. Capabilities The codet5-small model is capable of a variety of code-related tasks, including code summarization, code generation, code translation, code refinement, code defect detection, and code clone detection. It has been shown to outperform prior methods on these tasks, as the authors' experiments revealed that the model can better capture semantic information from code compared to previous approaches. What can I use it for? The primary use of the codet5-small model is to fine-tune it for a specific downstream task of interest, such as those mentioned above. You can find fine-tuned versions of the model on the Hugging Face Model Hub to get started. For example, you could fine-tune the codet5-small model on a code summarization dataset to create a model that can generate natural language descriptions for code snippets. Or you could fine-tune it on a code translation dataset to build a model that can translate between programming languages. Things to try One interesting aspect of the codet5-small model is its ability to distinguish code tokens that are identifiers and recover them when masked. You could experiment with this capability by masking out identifiers in your input code and seeing how well the model is able to fill them in. Another interesting direction would be to explore the model's performance on cross-lingual code-related tasks, such as translating code from one programming language to another. The authors note that the model was trained on a diverse set of programming languages, so it may have the capability to handle such tasks.
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codet5p-110m-embedding
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