---
license: bsd-3-clause
---

# CodeT5+ 220M Bimodal Models

## Model description

[CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models
with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_,
and _encoder-decoder_) to support a wide range of code understanding and generation tasks.
It is introduced in the paper:

[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf)
by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (*
indicates equal contribution).

Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of
pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code
matching_ to learn rich representations from both unimodal code data and bimodal code-text data.
Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model
components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale
up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture.
Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B)
following [Code Alpaca](https://github.com/sahil280114/codealpaca).

## How to use

This model can be easily loaded using the `AutoModel` functionality and employs the [CodeT5](https://github.com/salesforce/CodeT5) tokenizer with three special tokens added (`[ENC]`, `[TDEC]`, `[CDEC]`).
This checkpoint consists of a CodeT5+ 220M model and a projection layer and an itm_head layer for text-code matching.

```python
from transformers import AutoModel, AutoTokenizer

checkpoint = "Salesforce/codet5p-220m-bimodal"
device = "cuda"  # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
model = AutoModel.from_pretrained(checkpoint, trust_remote_code=True).to(device)
```

## Pretraining data

This checkpoint is trained on the stricter permissive subset of the deduplicated version of
the [github-code dataset](https://huggingface.co/datasets/codeparrot/github-code).
The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”,
“cc0-1.0”, “unlicense”, “isc”).
Supported languages (9 in total) are as follows:
`c`, `c++`, `c-sharp`,  `go`, `java`, `javascript`,  `php`, `python`, `ruby.`

## Training procedure

This checkpoint is first trained on the unimodal code data at the first-stage pretraining and then on bimodal text-code
pair data using the proposed mixture of pretraining tasks.
Please refer to the paper for more details.

## Evaluation results

Please refer to the paper and the official GitHub repo for more details.

## BibTeX entry and citation info

```bibtex
@article{wang2023codet5plus,
  title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
  author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
  journal={arXiv preprint},
  year={2023}
}
```