|
--- |
|
license: bsd-3-clause |
|
--- |
|
# CodeGen (CodeGen-NL 6B) |
|
|
|
## Model description |
|
|
|
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). |
|
|
|
The checkpoint included in this repository is denoted as **CodeGen-NL 6B** in the paper, where "NL" means it is pre-trained on the Pile and "6B" refers to the number of trainable parameters. |
|
|
|
## Training data |
|
|
|
This checkpoint (CodeGen-NL 6B) was pre-trained on [the Pile](https://github.com/EleutherAI/the-pile), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai/). Parts of the dataset include code data. |
|
|
|
## Training procedure |
|
|
|
CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. |
|
The family of models are trained using 4 TPU-v4 chips by Google, leveraging data and model parallelism. |
|
See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. |
|
|
|
## Evaluation results |
|
|
|
We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. |
|
|
|
|
|
## Intended Use and Limitations |
|
|
|
As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. |
|
However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. |
|
|
|
## How to use |
|
|
|
This model can be easily loaded using the `AutoModelForCausalLM` functionality: |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-nl') |
|
model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-6B-nl') |
|
|
|
text = "def hello_world():" |
|
input_ids = tokenizer(text, return_tensors="pt").input_ids |
|
generated_ids = model.generate(input_ids, max_length=128) |
|
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
|
``` |
|
|
|
## BibTeX entry and citation info |
|
|
|
```bibtex |
|
@article{Nijkamp2022ACP, |
|
title={A Conversational Paradigm for Program Synthesis}, |
|
author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, |
|
journal={arXiv preprint}, |
|
year={2022} |
|
} |
|
``` |
|
|
