Pycodet5

🚀 Model Card for PyCodeT5

CodeT5 Python Functions is a specialized variant of the CodeT5 model. It's fine - tuned for generating and understanding Python functions, helping to transform natural language into Python code and optimize existing code. Ideal for developers, learners, and AI - powered programming assistants.

🚀 Quick Start

To get started, use the code below to load and use the PyCodeT5 model.

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = 'Salesforce/CodeT5-Python-functions'
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example input
input_text = "def sum(a, b):"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate code
outputs = model.generate(**inputs)
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_code)

✨ Features

Direct Use

• Generate Python Functions: Convert natural language descriptions into functional Python code.
• Optimize Python Code: Apply Pythonic conventions and best practices to improve code quality.
• Assist with Debugging and Refactoring: Help users identify and fix issues in Python code.

Downstream Use [Optional]

• Integration with AI - powered programming assistants: Use as a backend model for intelligent code completion or review tools.

📦 Installation

No installation steps provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = 'Salesforce/CodeT5-Python-functions'
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example input
input_text = "def sum(a, b):"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate code
outputs = model.generate(**inputs)
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_code)

📚 Documentation

Model Details

CodeT5 Python Functions is a specialized variant of the CodeT5 model, fine - tuned for generating and understanding Python functions. It is designed to assist in transforming natural language descriptions into functional Python code, as well as optimizing existing code by applying Pythonic conventions and best practices. This model can generate function definitions, implement logical flows, and assist with debugging and refactoring Python code.

Uses

Direct Use

• Generate Python functions from natural language descriptions.
• Optimize Python code using Pythonic conventions.
• Assist with debugging and refactoring Python code.

Downstream Use [Optional]

• Integrate with AI - powered programming assistants as a backend model.

Out - of - Scope Use

• Not suitable for non - Python code generation.
• Not recommended for sensitive applications.

Bias, Risks, and Limitations

This model, like other large language models, may reflect biases present in the data used during training. For example, it may generate code that includes harmful stereotypes or unfair practices in certain contexts.

⚠️ Important Note

When applying the model in high - risk or security - critical environments, extra validation and review processes should be in place. Always ensure that code generated by this model undergoes thorough human review, especially in sensitive or production environments.

Training Details

Training Data

The model was fine - tuned on a dataset of Python code from various open - source repositories. It has been specifically trained to understand Python function structures and best practices.

Training Procedure

• Preprocessing: The training data underwent standard preprocessing steps, such as tokenization and cleaning, to ensure quality input for fine - tuning.
• Speeds, Sizes, Times: More detailed information on training speed and times is needed for transparency.

Evaluation

Testing Data, Factors & Metrics

Testing Data

The testing data consists of Python code from a variety of open - source repositories and function - oriented tasks.

Factors

• Task Complexity: Evaluation includes both simple function generation and more complex refactoring tasks.
• Code Quality: Assessed based on the application of Pythonic principles like readability, clarity, and efficiency.

Metrics

• Accuracy: Measures the correctness of the generated code.
• Code Quality: Evaluates how well the generated code follows Pythonic best practices.

Results

More information on the evaluation results is needed to fully assess the model’s performance.

Model Examination

A detailed examination of the model's behavior, including edge cases, is needed to identify areas of improvement.

Environmental Impact

• Hardware Type: More information needed
• Cloud Provider: More information needed
• Carbon Emitted: More information needed

Technical Specifications [Optional]

Model Architecture and Objective

The architecture is based on the Transformer model, optimized for code generation tasks.

Compute Infrastructure

More details about the compute resources used in training and deployment are needed.

Citation

BibTeX: More information needed. APA: More information needed.

Model Card Authors [Optional]

S de Jager

Model Card Contact

More information needed.