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---
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)
```
## Prompt
You could give model a style or a specific language, for example:
```python
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)
``` |