---
tags:
- summarization
widget:
- text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b"

---


# CodeTrans model for program synthesis

## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Citation Information](#citation-information)

## Model Details
- **Model Description:** This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the program synthesis task for the lisp inspired DSL code.
- **Developed by:** [Ahmed Elnaggar](https://www.linkedin.com/in/prof-ahmed-elnaggar/),[Wei Ding](https://www.linkedin.com/in/wei-ding-92561270/)
- **Model Type:** Summarization
- **Language(s):** English
- **License:** Unknown
- **Resources for more information:**
	- [Research Paper](https://arxiv.org/pdf/2104.02443.pdf)
    - [GitHub Repo](https://github.com/agemagician/CodeTrans)


## How to Get Started With the Model

Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:

```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune", skip_special_tokens=True),
    device=0
)

tokenized_code = "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/transfer%20learning%20fine-tuning/small_model.ipynb).
## Training data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)




## Uses

#### Direct Use

The model could be used to generate lisp inspired DSL code given the human language description tasks.


## Training

#### Training Data

The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)

The authors provide additionally notes about the vocabulary used, in the [associated paper](https://arxiv.org/pdf/2104.02443.pdf): 

> We used the SentencePiece model (Kudo, 2018) to construct the vocabulary for this research, as well as to decode and encode the input/output.


## Training procedure

#### Preprocessing

##### Transfer-learning Pretraining

The model was trained on a single TPU Pod V3-8 for 500,000 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 5,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing lisp inspired DSL data.


## Evaluation

#### Results

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

Test results :

|   Language / Model   |      LISP      |
| -------------------- | :------------: |
|   CodeTrans-ST-Small    |     89.43      |
|   CodeTrans-ST-Base     |     89.65      |
|   CodeTrans-TF-Small    |     90.30      |
|   CodeTrans-TF-Base     |     90.24      |
|   CodeTrans-TF-Large    |     90.21      |
|   CodeTrans-MT-Small    |     82.88      |
|   CodeTrans-MT-Base     |     86.99      |
|   CodeTrans-MT-Large    |     90.27      |
|   CodeTrans-MT-TF-Small |   **90.31**    |
|   CodeTrans-MT-TF-Base  |     90.30      |
|   CodeTrans-MT-TF-Large |     90.17      |
|   State of the art   |     85.80      |


## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf).


- **Hardware Type:** Nvidia RTX 8000 GPUs

- **Hours used:** Unknown

- **Cloud Provider:** GCC TPU v2-8 and v3-8. 

- **Compute Region:** Unknown

- **Carbon Emitted:** Unknown 

## Citation Information

```bibtex
@misc{elnaggar2021codetrans,
      title={CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing}, 
      author={Ahmed Elnaggar and Wei Ding and Llion Jones and Tom Gibbs and Tamas Feher and Christoph Angerer and Silvia Severini and Florian Matthes and Burkhard Rost},
      year={2021},
      eprint={2104.02443},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}
```