metadata
datasets:
- code_search_net
widget:
- text: >-
def <mask> ( a, b ) : if a > b : return a else return b</s>return the
maximum value
- text: 'def <mask> ( a, b ) : if a > b : return a else return b'
Model Architecture
This model follows the distilroberta-base architecture. Futhermore, this model was initialized with the checkpoint of distilroberta-base.
Pre-training phase
This model was pre-trained with the MLM objective (mlm_probability=0.15
).
During this phase, the inputs had two formats. One is the following: where $t_1, \dots, t_n$ are the code tokens and $w_1, \dots, w_m$ are the natural language description tokens. More concretely, this is the snippet that tokenizes the input:
def tokenize_function_bimodal(examples, tokenizer, max_len):
codes = [' '.join(example) for example in examples['func_code_tokens']]
nls = [' '.join(example) for example in examples['func_documentation_tokens']]
pairs = [[c, nl] for c, nl in zip(codes, nls)]
return tokenizer(pairs, max_length=max_len, padding="max_length", truncation=True)
The other format is: where $t_1, \dots, t_n$ are the code tokens. More concretely, this is the snippet that tokenizes the input:
def tokenize_function_unimodal(examples, tokenizer, max_len, tokens_column):
codes = [' '.join(example) for example in examples[tokens_column]]
return tokenizer(codes, max_length=max_len, padding="max_length", truncation=True)
Training details
- Max length: 512
- Effective batch size: 64
- Total steps: 140000
- Learning rate: 5e-4
Usage
model = AutoModelForMaskedLM.from_pretrained('antolin/distilroberta-base-csn-python-unimodal-bimodal')
tokenizer = AutoTokenizer.from_pretrained('antolin/distilroberta-base-csn-python-unimodal-bimodal')
mask_filler = pipeline("fill-mask", model=model, tokenizer=tokenizer)
code_tokens = ["def", "<mask>", "(", "a", ",", "b", ")", ":", "if", "a", ">", "b", ":", "return", "a", "else", "return", "b"]
nl_tokens = ["return", "the", "maximum", "value"]
input_text = ' '.join(code_tokens) + tokenizer.sep_token + ' '.join(nl_tokens)
pprint(mask_filler(input_text, top_k=5))
[{'score': 0.7177600860595703,
'sequence': 'def maximum ( a, b ) : if a > b : return a else return breturn '
'the maximum value',
'token': 4532,
'token_str': ' maximum'},
{'score': 0.22075247764587402,
'sequence': 'def max ( a, b ) : if a > b : return a else return breturn the '
'maximum value',
'token': 19220,
'token_str': ' max'},
{'score': 0.015111264772713184,
'sequence': 'def minimum ( a, b ) : if a > b : return a else return breturn '
'the maximum value',
'token': 3527,
'token_str': ' minimum'},
{'score': 0.007394665852189064,
'sequence': 'def min ( a, b ) : if a > b : return a else return breturn the '
'maximum value',
'token': 5251,
'token_str': ' min'},
{'score': 0.004020793363451958,
'sequence': 'def length ( a, b ) : if a > b : return a else return breturn '
'the maximum value',
'token': 5933,
'token_str': ' length'}]