codet5-small - Generate_Docstrings_for_Python - Condensed Open - Source Model - Free Generation of Docstrings for Python Functions

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Codet5 Small Generate Docstrings For Python Condensed

Developed by DunnBC22

A model fine-tuned based on Salesforce/codet5-small for generating docstrings for Python functions

Text Generation

Transformers

EnglishOpen Source License:Apache-2.0 #Python documentation generation #Automated code commenting #Rouge optimization

Downloads 20

Release Time : 4/6/2023

Model Overview

This model is specifically designed to automatically generate corresponding docstrings based on Python function code, serving as part of code documentation tools.

Model Features

Condensed optimization

Trimmed longer samples to optimize training speed on consumer-grade hardware

Documentation generation capability

Can automatically generate corresponding docstrings based on Python function code

Fine-tuning optimization

Fine-tuned on specific datasets based on the CodeT5-small model

Model Capabilities

Python code analysis

Docstring generation

Text-to-text conversion

Use Cases

Code documentation

Automatic function documentation generation

Automatically generates descriptive docstrings for Python functions

Rouge1 score 0.3828, Rouge2 score 0.2214

Development tools

IDE plugin

Can be integrated into development environments for automatic code documentation generation

🚀 codet5-small-Generate_Docstrings_for_Python-Condensed

This model is a fine - tuned version of Salesforce/codet5-small, designed to predict docstrings for Python functions, offering efficient performance on consumer hardware.

🚀 Quick Start

This model is a fine - tuned version of Salesforce/codet5-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.1444
Rouge1: 0.3828
Rouge2: 0.2214
Rougel: 0.3583
Rougelsum: 0.3661
Gen Len: 12.6656

✨ Features

This model is trained to predict the docstring (the output) for a function (the input).

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Generate%20Docstrings/Smol%20Dataset/Code_T5_Project-Small%20Checkpoint.ipynb

For this model, the author trimmed some of the longer samples to quicken the pace of training on consumer hardware.

📚 Documentation

Intended uses & limitations

This model is intended to demonstrate the author's ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: calum/the-stack-smol-python-docstrings (from HuggingFace Datasets; https://huggingface.co/datasets/calum/the-stack-smol-python-docstrings)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.9064	1.0	965	2.3096	0.3695	0.2098	0.3464	0.3529	11.7285
2.4836	2.0	1930	2.2051	0.38	0.2176	0.3554	0.3635	12.9401
2.3669	3.0	2895	2.1548	0.3842	0.2219	0.3595	0.3674	13.0029
2.3254	4.0	3860	2.1444	0.3828	0.2214	0.3583	0.3661	12.6656

Framework versions

Transformers 4.26.1
Pytorch 1.12.1
Datasets 2.9.0
Tokenizers 0.12.1

📄 License

This project is licensed under the Apache - 2.0 license.

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