README.md

---
license: apache-2.0
widget:
- text: "<|endoftext|>\nfunction getDateAfterNDay(n){\n    return moment().add(n, 'day')\n}\n// docstring\n/**"
---

## Basic info

model based [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono)

fine-tuned with data [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean)

data filter by JavaScript and TypeScript

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_type = 'kdf/javascript-docstring-generation'
tokenizer = AutoTokenizer.from_pretrained(model_type)
model = AutoModelForCausalLM.from_pretrained(model_type)

inputs = tokenizer('''<|endoftext|>
function getDateAfterNDay(n){
    return moment().add(n, 'day')
}

// docstring
/**''', return_tensors='pt')

doc_max_length = 128

generated_ids = model.generate(
    **inputs,
    max_length=inputs.input_ids.shape[1] + doc_max_length,
    do_sample=False,
    return_dict_in_generate=True,
    num_return_sequences=1,
    output_scores=True,
    pad_token_id=50256,
    eos_token_id=50256  # <|endoftext|>
)

ret = tokenizer.decode(generated_ids.sequences[0], skip_special_tokens=False)
print(ret)

```