Gemini Open-source Code Abstract Generation Model - Free support for multi-language code explanation function

Gemini

Developed by describeai

A T5 architecture-based code summarization generation model supporting explanations for multiple programming languages

EnglishOpen Source License:MIT #Code summarization generation #Multilingual code explanation #T5 architecture optimization

Downloads 844

Release Time : 3/2/2022

Model Overview

Gemini is a Transformer model based on Google's T5 architecture, capable of generating English summaries/explanations for code snippets in Python, JavaScript and other programming languages.

Model Features

Multilingual code support

Supports code explanations for 5 programming languages including Python and JavaScript

Dual version options

Provides two size options: 770M parameter standard version and 220M parameter lite version

Pretraining + fine-tuning

Pretrained on 800,000 code/description pairs and fine-tuned on 10,000 synthetic data samples

Model Capabilities

Code summarization generation

Code explanation

Document generation

Synthetic data production

Use Cases

Code understanding

Simple code explanation

Generates 1-2 sentence explanations for short to medium-length code snippets

Outputs concise and understandable functional descriptions of code

Document generation

Automatic documentation creation

Generates basic documentation for codebases

Provides preliminary functional descriptions of code

🚀 Gemini

Gemini is a transformer - based model for code summarization and explanation, offering support for multiple programming languages.

🚀 Quick Start

For in - depth understanding of our model and methods, please see our blog here

✨ Features

Multi - language Support: Gemini can summarize and explain short - to - medium code snippets in Python, Javascript, Java, Ruby, and Go, and outputs descriptions in English.
Based on T5: It is a transformer based on Google's T5 model, pre - trained on approximately 800k code/description pairs and fine - tuned on 10k synthetically generated higher - level explanations.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

You can use this model directly with a pipeline for Text2Text generation, as shown below:

from transformers import pipeline, set_seed

summarizer = pipeline('text2text-generation', model='describeai/gemini')
code = "print('hello world!')"

response = summarizer(code, max_length=100, num_beams=3)
print("Summarized code: " + response[0]['generated_text'])

Which should yield something along the lines of:

Summarized code: The following code is greeting the world.

📚 Documentation

Intended Uses

Gemini without any additional fine - tuning is capable of explaining code in a sentence or two and typically performs best in Python and Javascript. We recommend using Gemini for either simple code explanation, documentation or producing more synthetic data to improve its explanations.

Model Sizes

Gemini (this repo): 770 Million Parameters
Gemini - Small: 220 Million Parameters

Limitations

Typically, Gemini may produce overly simplistic descriptions that don't encompass the entire code snippet. We suspect with more training data, this could be circumvented and will produce better results.

About Us

At Describe.ai, we are focused on building Artificial Intelligence systems that can understand language as well as humans. While a long path, we plan to contribute our findings to our API to the Open Source community.

📄 License

This project is licensed under the MIT license.

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