CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. CodeGeeX supports 15+ programming languages for both code generation and translation


THUDM/CodeGeeX is a Hugging Face model space containing pre-trained models and code snippets for natural language processing tasks, specifically focused on text generation and language translation. The models in this space are based on deep learning architectures and can be fine-tuned on specific tasks. The CodeGeeX models are optimized for performance and efficiency, making them suitable for deployment in production systems. The pre-trained models can generate coherent text in various languages and styles, and also support translation between multiple languages. This model space also includes a range of code snippets for text processing tasks, such as data preprocessing, text classification, and sentiment analysis.




Q What programming languages are supported by CodeGeeX?

CodeGeeX is a model space for natural language processing (NLP) models and code snippets, so the programming languages used to implement these models and snippets depend on the specific task at hand. However, many NLP models are implemented using Python, and CodeGeeX includes several pre-trained models and code snippets in Python.

Q Can I fine-tune the pre-trained models in CodeGeeX on my own data?

Yes! In fact, fine-tuning pre-trained models on task-specific data is a common way to improve performance on downstream NLP tasks. CodeGeeX includes instructions and examples for fine-tuning the pre-trained models on your own data, so you can adapt the models to your specific use case.

Q How does CodeGeeX compare to other NLP model spaces?

CodeGeeX is designed to be efficient and optimized for production use cases, making it a good choice for developers who need to deploy NLP models in real-world applications. Additionally, CodeGeeX includes a variety of pre-trained models and code snippets for text generation and language translation, making it a comprehensive resource for NLP tasks. However, depending on your specific use case, other model spaces may be more appropriate.

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