kdf/javascript-docstring-generation

Basic info

model based Salesforce/codegen-350M-mono

fine-tuned with data codeparrot/github-code-clean

data filter by JavaScript and TypeScript

Usage

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)

Prompt

You could give model a style or a specific language, for example:

inputs = tokenizer('''<|endoftext|>
function add(a, b){
    return a + b;
}
// docstring
/**
  * Calculate number add.
  * @param a {number} the first number to add
  * @param b {number} the second number to add
  * @return the result of a + b
  */
<|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)

inputs = tokenizer('''<|endoftext|>
function add(a, b){
    return a + b;
}
// docstring
/**
  * 计算数字相加
  * @param a {number} 第一个加数
  * @param b {number} 第二个加数
  * @return 返回 a + b 的结果
  */
<|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)