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| title: Code Generation with CodeT5 | |
| emoji: 😻 | |
| colorFrom: yellow | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.34.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| hf_oauth: true | |
| hf_oauth_scopes: | |
| - inference-api | |
| short_description: Leverage CodeT5-base for code generation tasks. | |
| model_info: | |
| model_name: Salesforce/codet5-base | |
| model_type: Encoder-Decoder Transformer | |
| architecture: T5-based | |
| pretraining_tasks: | |
| - Denoising | |
| - Bimodal Dual Generation | |
| training_data: | |
| - CodeSearchNet | |
| - CodeXGLUE | |
| fine_tuning_tasks: | |
| - Code Summarization | |
| - Code Generation | |
| - Code Translation | |
| performance_benchmarks: | |
| - CodeXGLUE | |
| paper: >- | |
| CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code | |
| Understanding and Generation | |
| publication_date: '2021-09-02' | |
| arxiv_url: https://arxiv.org/abs/2109.00859 | |
| github_url: https://github.com/salesforce/CodeT5 | |
| huggingface_url: https://huggingface.co/Salesforce/codet5-base | |
| # 🚀 Code Generation with CodeT5 | |
| Welcome to the **Code Generation with CodeT5** project! This repository demonstrates how to leverage the `Salesforce/codet5-base` model for generating Python code snippets based on textual prompts. The project utilizes Gradio for creating interactive web interfaces and is deployed on Hugging Face Spaces. | |
| ## 📚 Repository Contents | |
| - **Model Configuration:** | |
| Stored in `config.json`, this file defines the architecture and settings of the CodeT5 model. | |
| - **Tokenizer Special Tokens:** | |
| Located in `special_tokens_map.json`, it maps special tokens used during tokenization. | |
| - **Training Hyperparameters:** | |
| Found in `training_args.json`, this file contains parameters like learning rate, batch size, and number of epochs used during training. | |
| - **Inference Code:** | |
| The `app.py` script loads the model and provides an interface for code generation. | |
| - **Dependencies:** | |
| Listed in `requirements.txt`, these are the necessary packages for running the model. | |
| - **Documentation:** | |
| This `README.md` provides an overview and guide for setting up and using the repository. | |
| ## 🔧 Setup & Usage | |
| ### 1. Clone the Repository | |
| Clone the repository to your local machine: | |
| ```bash | |
| git clone https://github.com/your-username/codegen-model-repo.git | |
| cd codegen-model-repo | |
| ``` | |
| ### 2. Install Dependencies | |
| Install the required packages using pip: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 3. Run the Gradio App | |
| Launch the Gradio app to start generating code: | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| Access the app in your browser to input prompts and receive generated code snippets. | |
| ## 🌐 Deploying on Hugging Face Spaces | |
| To deploy your Gradio app on Hugging Face Spaces: | |
| 1. **Create a New Space:** | |
| - Visit [Hugging Face Spaces](https://huggingface.co/spaces) and create a new Space. | |
| - Select Gradio as the SDK. | |
| 2. **Push Your Code:** | |
| - Initialize a Git repository in your project directory. | |
| - Commit your code and push it to the new Space's repository. | |
| For a detailed walkthrough on deploying Gradio apps to Hugging Face Spaces, refer to this [tutorial](https://pyimagesearch.com/2024/12/30/deploy-gradio-apps-on-hugging-face-spaces/). | |
| ## 📄 License | |
| This project is licensed under the MIT License. |