language: code
tags:
- summarization
widget:
- text: select time ( col0 ) from tab0
CodeTrans model for source code summarization sql
Pretrained model on programming language sql using the t5 base model architecture. It was first released in this repository. This model is trained on tokenized sql code functions: it works best with tokenized sql functions.
Model description
This CodeTrans model is based on the t5-base model. It has its own SentencePiece vocabulary model. It used single-task training on source code summarization sql dataset.
Intended uses & limitations
The model could be used to generate the description for the sql function or be fine-tuned on other sql code tasks. It can be used on unparsed and untokenized sql code. However, if the sql code is tokenized, the performance should be better.
How to use
Here is how to use this model to generate sql function documentation using Transformers SummarizationPipeline:
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
pipeline = SummarizationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_sql"),
tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_sql", skip_special_tokens=True),
device=0
)
tokenized_code = "select time ( col0 ) from tab0"
pipeline([tokenized_code])
Run this example in colab notebook.
Training data
The supervised training tasks datasets can be downloaded on Link
Evaluation results
For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score):
Test results :
| Language / Model | Python | SQL | C# |
|---|---|---|---|
| CodeTrans-ST-Small | 8.45 | 17.55 | 19.74 |
| CodeTrans-ST-Base | 9.12 | 15.00 | 18.65 |
| CodeTrans-TF-Small | 10.06 | 17.71 | 20.40 |
| CodeTrans-TF-Base | 10.94 | 17.66 | 21.12 |
| CodeTrans-TF-Large | 12.41 | 18.40 | 21.43 |
| CodeTrans-MT-Small | 13.11 | 19.15 | 22.39 |
| CodeTrans-MT-Base | 13.37 | 19.24 | 23.20 |
| CodeTrans-MT-Large | 13.24 | 19.40 | 23.57 |
| CodeTrans-MT-TF-Small | 12.10 | 18.25 | 22.03 |
| CodeTrans-MT-TF-Base | 10.64 | 16.91 | 21.40 |
| CodeTrans-MT-TF-Large | 12.14 | 19.98 | 21.10 |
| CODE-NN | -- | 18.40 | 20.50 |
Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn