Fine-tuned-CodeT5 (fine-tune the CodeT5-base model)

Fine-tuned CodeT5 model. The code for fine-tuning is released in this repository

The pre-trained model was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in this repository.

Model description

This model is fine-tuned on CodeT5-base to accomplish the transfer-learning downstream task-Python code generation. CodeT5 is a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers. Comprehensive experiments show that CodeT5 significantly outperforms prior methods on understanding tasks such as code defect detection and clone detection, and generation tasks across various directions including PL-NL, NL-PL, and PL-PL.

How to use

Here is how to use this model:

from transformers import RobertaTokenizer, T5ForConditionalGeneration

tokenizer = RobertaTokenizer.from_pretrained('gangqinxiao13/fine-tuned-codet5')
model = T5ForConditionalGeneration.from_pretrained('gangqinxiao13/fine-tuned-codet5')

text = "print hello"
task_prefix = "Generate Python code from natural language:"
input = tokenizer(
        [task_prefix + input],
        return_tensors="pt",
        padding='max_length',
        max_length=512,
        truncation=True
    )
input_ids = tokenizer(text, return_tensors="pt").input_ids

outputs = model.generate(
    input_ids=input['input_ids'],
    attention_mask=input["attention_mask"],
    do_sample=False,
    max_new_tokens=512
    )
generated_code = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(generated_code[0]) 

Pre-training data

The CodeT5 model was pretrained on CodeSearchNet Husain et al., 2019. Additionally, the authors collected two datasets of C/CSharp from BigQuery1 to ensure that all downstream tasks have overlapped programming languages with the pre-training data. In total, around 8.35 million instances are used for pretraining.

Fine-tuning data

The fine-tuned model was trained on conala-mined Yin et al., 2019, Re-sampled Python3.7 API Knowledge Xu et al., 2020 and MBPP (Mostly Basic Python Programming) Austin et al., 2021