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A pre-trained model for programming and natural languages.

CodeBERT is a pre-trained model for programming languages, adept at understanding and generating code by leveraging a multi-lingual approach across Python, Java, JavaScript, PHP, Ruby, and Go. It utilizes the `transformers` framework and can be employed as a pre-trained Roberta base model. Its architecture is designed to produce embeddings for both natural language (NL) and programming language (PL) segments. CodeBERT is utilized for tasks like code search, documentation generation, and masked language modeling. Enhanced versions such as GraphCodeBERT incorporate data flow analysis for improved code representation. Other models in the series include UniXcoder, CodeReviewer, CodeExecutor and LongCoder, each designed for specific use-cases such as code review and long code modeling.
CodeBERT is a pre-trained model for programming languages, adept at understanding and generating code by leveraging a multi-lingual approach across Python, Java, JavaScript, PHP, Ruby, and Go.
Explore all tools that specialize in generate code documentation. This domain focus ensures CodeBERT delivers optimized results for this specific requirement.
Explore all tools that specialize in automate code reviews. This domain focus ensures CodeBERT delivers optimized results for this specific requirement.
Explore all tools that specialize in masked language modeling. This domain focus ensures CodeBERT delivers optimized results for this specific requirement.
Trained on NL-PL pairs in 6 programming languages (Python, Java, JavaScript, PHP, Ruby, Go), enabling cross-lingual code understanding and generation.
Incorporates data flow analysis into the pre-training process, enabling a more nuanced understanding of code structure and dependencies.
Supports both code-related understanding and generation tasks through a unified cross-modal pre-training approach.
Pre-trained with code change and code review data to support automated code review activities.
Utilizes a sparse and efficient Transformer model for long code modeling, allowing for more effective code completion in large projects.
Install PyTorch: `pip install torch`
Install Transformers: `pip install transformers`
Load the tokenizer: `tokenizer = RobertaTokenizer.from_pretrained("microsoft/codebert-base")`
Load the model: `model = RobertaModel.from_pretrained("microsoft/codebert-base")`
Move the model to the appropriate device (CPU or GPU): `model.to(device)`
Tokenize input text or code using the tokenizer.
Obtain embeddings by passing token IDs through the model.
Utilize these embeddings for downstream tasks like code search or documentation generation.
All Set
Ready to go
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"Generally well-regarded for its versatility and performance in code-related tasks."
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Effortlessly find and manage open-source dependencies for your projects.

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