CodeGen-350M-multi-xlcost

CodeGen-350M-multi-xlcost is a CodeGen model fine-tuned on the Python split of XLCost dataset.

Usage

You can load the CodeGen-350M-multi-xlcost model and tokenizer directly in transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("giulio98/codegen-350M-multi-xlcost")
model = AutoModelForCausalLM.from_pretrained("giulio98/codegen-350M-multi-xlcost")

text = tokenizer.eos_token + "\'\'\'\n" + "function to add two numbers" + "\n\'\'\'\n" + "###\n"
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))

Output:

'''
function to add two numbers 
'''
###
def add(a, b):
    return a + b

Training

The model was finetuned on XLCost-single-prompt, an improved version of the original XLCost dataset xlcost-text-to-code. Below the hyperparameters.

The training was executed on 1 x V100 (16GB) GPU for 6h 42m

Performance

We evaluated the model on the first 400 samples of XLCOST's XLCost-single-prompt test split and comparing the outputs of the generated codes with respect to the expected output using pass@k metric.

The pass@k metric tells the probability that at least one out of k generations passes the tests.

Citations

@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}
}