Code Trans T5 Base Code Documentation Generation Go Multitask Finetune

🚀 CodeTrans Model for Go Code Documentation Generation

This is a pre-trained model for the Go programming language, leveraging the T5 base model architecture. It was initially released in this repository. This model is trained on tokenized Go code functions, and it performs best with tokenized Go functions.

✨ Features

• Based on the t5-base model with its own SentencePiece vocabulary model.
• Utilizes multi - task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets.
• Fine - tuned for the code documentation generation task of Go functions/methods.

📦 Installation

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

💻 Usage Examples

Basic Usage

Here is how to use this model to generate Go function documentation using Transformers SummarizationPipeline:

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_go_multitask_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_go_multitask_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot   }"
pipeline([tokenized_code])

Run this example in colab notebook.

📚 Documentation

Model Description

This CodeTrans model is based on the t5-base model. It has its own SentencePiece vocabulary model. It used multi - task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine - tuned on the code documentation generation task for the Go function/method.

Intended Uses & Limitations

The model could be used to generate the description for the Go function or be fine - tuned on other Go code tasks. It can be used on unparsed and untokenized Go code. However, if the Go code is tokenized, the performance should be better.

🔧 Technical Details

Training Data

The supervised training tasks datasets can be downloaded on Link

Training Procedure

Multi - task Pretraining

The model was trained on a single TPU Pod V3 - 8 for half million steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder - decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre - training.

Fine - tuning

This model was then fine - tuned on a single TPU Pod V2 - 8 for 2000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing Go code.

Evaluation Results

For the code documentation tasks, different models achieve the following results on different programming languages (in BLEU score):

Test results:

📄 License

No license information is provided in the original document, so this section is skipped.

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn