README.md

---
language: code
tags:
- code
- gpt2
- generation
datasets:
- giulio98/xlcost-single-prompt
widget:
- text: "'''\nfunction to add two numbers\n'''\n###\n"
  example_title: "add two numbers"
model-index:
- name: codegen-350M-multi-xlcost
  results:
  - task: 
      name: Code Generation
      type: code-generation
    dataset:
      name: "XLCost" 
      type: code_eval_outputs
    metrics:
       - name: pass@1
         type: code_eval_outputs
         value: 3.70
       - name: pass@10
         type: code_eval_outputs
         value: 14.5
---

# 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`:

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

The model was finetuned on [XLCost-single-prompt](https://huggingface.co/datasets/giulio98/xlcost-single-prompt), an improved version of the original XLCost dataset [
xlcost-text-to-code](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code). Below the hyperparameters.

| Hyperparameter | value |
|---------------------------|--------|
|Per device train batch size| 8 |
|Context size| 1024 |
|Training steps| 258|
|Gradient accumulation| 4|
|Gradient checkpointing| True|
|Learning rate|1.8e-05 |
|Weight decay | 0.0 |
|Warmup steps| 10 |
|Schedule| linear |

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](https://huggingface.co/datasets/giulio98/xlcost-single-prompt/viewer/Python/test) and comparing the outputs of the generated codes with respect to the expected output using pass@k metric.

| Metric | codegen-350M-multi-xlcost | codegen-350M-mono(zero-shot) | codegen-350M-mono (one-shot) | codegen-350M-mono(few-shot)
|--------|-----|-----|-----|-----|
|pass@1 | 3.70% | 0.4% | 0.35% | 0.48% |
|pass@10 | 14.5% | 3.5% | 3 % | 3.75% |

The [pass@k metric](https://huggingface.co/metrics/code_eval) 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}
}
```