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refactor ScriptTensorizer with general tensorize API #1117
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…h#1117) Summary: Pull Request resolved: facebookresearch#1117 This diff introduced a general API for handling different inputs. In most general case, we would expect inputs to be either 1) multiple rows, each row contains a list of text (in most case it is single sentence or a pair) ===> List[List[str]] 2) multiple rows, each row contains a list of pre-processes tokens (in most case it is single sentence or a pair) ===> List[List[List[str]]] For single sentence classification task, we would expect inputs to be either 1) multiple rows, each row contains a single text ===> List[str] 2) multiple rows, each row contains a single pre-processed tokens ===> List[List[str]] This refactoring provides two general API 1) def tensorize( self, texts_list: Optional[List[List[str]]] = None, tokens_list: Optional[List[List[List[str]]]] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: 2) def tensorize_single( self, texts_list: Optional[List[str]] = None, tokens_list: Optional[List[List[str]]] = None, ): And internally it will automate handle the passed inputs is texts or tokens Differential Revision: D18386345 fbshipit-source-id: 90d19a7d8dad57d16f274a3b389445fa71c8d105
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…h#1117) Summary: Pull Request resolved: facebookresearch#1117 This diff introduced a general API for handling different inputs. In most general case, we would expect inputs to be either 1) multiple rows, each row contains a list of text (in most case it is single sentence or a pair) ===> List[List[str]] 2) multiple rows, each row contains a list of pre-processes tokens (in most case it is single sentence or a pair) ===> List[List[List[str]]] For single sentence classification task, we would expect inputs to be either 1) multiple rows, each row contains a single text ===> List[str] 2) multiple rows, each row contains a single pre-processed tokens ===> List[List[str]] This refactoring provides two general API 1) def tensorize( self, texts_list: Optional[List[List[str]]] = None, tokens_list: Optional[List[List[List[str]]]] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: 2) def tensorize_single( self, texts_list: Optional[List[str]] = None, tokens_list: Optional[List[List[str]]] = None, ): And internally it will automate handle the passed inputs is texts or tokens Differential Revision: D18386345 fbshipit-source-id: 7f767e3958d3053801137e454f15a1dd5ae37757
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…h#1117) Summary: Pull Request resolved: facebookresearch#1117 This diff introduced a general API for handling different inputs. In most general case, we would expect inputs to be either 1) multiple rows, each row contains a list of text (in most case it is single sentence or a pair) ===> List[List[str]] 2) multiple rows, each row contains a list of pre-processes tokens (in most case it is single sentence or a pair) ===> List[List[List[str]]] For single sentence classification task, we would expect inputs to be either 1) multiple rows, each row contains a single text ===> List[str] 2) multiple rows, each row contains a single pre-processed tokens ===> List[List[str]] This refactoring provides two general API 1) def tensorize( self, texts_list: Optional[List[List[str]]] = None, tokens_list: Optional[List[List[List[str]]]] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: 2) def tensorize_single( self, texts_list: Optional[List[str]] = None, tokens_list: Optional[List[List[str]]] = None, ): And internally it will automate handle the passed inputs is texts or tokens Differential Revision: D18386345 fbshipit-source-id: 0061b0968b908c1e7d08bc2f73759e1ebd74b9f4
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Summary:
This diff introduced a general API for handling different inputs.
In most general case, we would expect inputs to be either
For single sentence classification task, we would expect inputs to be either
This refactoring provides two general API
def tensorize(
self,
texts_list: Optional[List[List[str]]] = None,
tokens_list: Optional[List[List[List[str]]]] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
def tensorize_single(
self,
texts_list: Optional[List[str]] = None,
tokens_list: Optional[List[List[str]]] = None,
):
And internally it will automate handle the passed inputs is texts or tokens
Differential Revision: D18386345