def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
attribution_type: str = "lig",
):
"""
Args:
model (PreTrainedModel): Pretrained huggingface Question Answering model.
tokenizer (PreTrainedTokenizer): Pretrained huggingface tokenizer
attribution_type (str, optional): The attribution method to calculate on. Defaults to "lig".
Raises:
AttributionTypeNotSupportedError: [description]
"""
super().__init__(model, tokenizer)
if attribution_type not in SUPPORTED_ATTRIBUTION_TYPES:
raise AttributionTypeNotSupportedError(
f"""Attribution type '{attribution_type}' is not supported.
Supported types are {SUPPORTED_ATTRIBUTION_TYPES}""")
self.attribution_type = attribution_type
self.attributions: Union[None, LIGAttributions] = None
self.start_attributions = None
self.end_attributions = None
self.input_ids: torch.Tensor = torch.Tensor()
self.position = 0
def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
attribution_type: str = "lig",
):
"""
Args:
model (PreTrainedModel): Pretrained huggingface Sequence Classification model.
tokenizer (PreTrainedTokenizer): Pretrained huggingface tokenizer
attribution_type (str, optional): The attribution method to calculate on. Defaults to "lig".
Raises:
AttributionTypeNotSupportedError:
"""
super().__init__(model, tokenizer)
if attribution_type not in SUPPORTED_ATTRIBUTION_TYPES:
raise AttributionTypeNotSupportedError(
f"""Attribution type '{attribution_type}' is not supported.
Supported types are {SUPPORTED_ATTRIBUTION_TYPES}""")
self.attribution_type = attribution_type
self.label2id = model.config.label2id
self.id2label = model.config.id2label
self.attributions: Union[None, LIGAttributions] = None
self.input_ids: torch.Tensor = torch.Tensor()
self._single_node_output = False
def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
attribution_type: str = "lig",
):
"""
Args:
model (PreTrainedModel):Pretrained huggingface Sequence Classification model. Must be a NLI model.
tokenizer (PreTrainedTokenizer): Pretrained huggingface tokenizer
attribution_type (str, optional): The attribution method to calculate on. Defaults to "lig".
Raises:
AttributionTypeNotSupportedError: [description]
ValueError: [description]
"""
super().__init__(model, tokenizer)
if attribution_type not in SUPPORTED_ATTRIBUTION_TYPES:
raise AttributionTypeNotSupportedError(
f"""Attribution type '{attribution_type}' is not supported.
Supported types are {SUPPORTED_ATTRIBUTION_TYPES}""")
self.label_exists, self.entailment_key = self._entailment_label_exists(
)
if not self.label_exists:
raise ValueError(
'Expected label "entailment" in `model.label2id` ')
self.entailment_idx = self.label2id[self.entailment_key]
self.include_hypothesis = False
self.attributions = []
self.internal_batch_size = None
self.n_steps = 50
def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
attribution_type: str = "lig",
custom_labels: Optional[List[str]] = None,
):
"""
Args:
model (PreTrainedModel): Pretrained huggingface Sequence Classification model.
tokenizer (PreTrainedTokenizer): Pretrained huggingface tokenizer
attribution_type (str, optional): The attribution method to calculate on. Defaults to "lig".
custom_labels (List[str], optional): Applies custom labels to label2id and id2label configs.
Labels must be same length as the base model configs' labels.
Labels and ids are applied index-wise. Defaults to None.
Raises:
AttributionTypeNotSupportedError:
"""
super().__init__(model, tokenizer)
if attribution_type not in SUPPORTED_ATTRIBUTION_TYPES:
raise AttributionTypeNotSupportedError(
f"""Attribution type '{attribution_type}' is not supported.
Supported types are {SUPPORTED_ATTRIBUTION_TYPES}"""
)
self.attribution_type = attribution_type
if custom_labels is not None:
if len(custom_labels) != len(model.config.label2id):
raise ValueError(
f"""`custom_labels` size '{len(custom_labels)}' should match pretrained model's label2id size
'{len(model.config.label2id)}'"""
)
self.id2label, self.label2id = self._get_id2label_and_label2id_dict(
custom_labels
)
else:
self.label2id = model.config.label2id
self.id2label = model.config.id2label
self.attributions: Union[None, LIGAttributions] = None
self.input_ids: torch.Tensor = torch.Tensor()
self._single_node_output = False
self.internal_batch_size = None
self.n_steps = 50
def __init__(
self,
text: str,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
attribution_type: str = "lig",
):
super().__init__(text, model, tokenizer)
if attribution_type not in SUPPORTED_ATTRIBUTION_TYPES:
raise AttributionTypeNotSupportedError(
f"""Attribution type '{attribution_type}' is not supported.
Supported types are {SUPPORTED_ATTRIBUTION_TYPES}""")
self.attribution_type = attribution_type
self.label2id = model.config.label2id
self.id2label = model.config.id2label
self.attributions = None
self.input_ids = None
def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
attribution_type: str = "lig",
):
super().__init__(model, tokenizer)
if attribution_type not in SUPPORTED_ATTRIBUTION_TYPES:
raise AttributionTypeNotSupportedError(
f"""Attribution type '{attribution_type}' is not supported.
Supported types are {SUPPORTED_ATTRIBUTION_TYPES}""")
self.attribution_type = attribution_type
self.attributions: Union[None, LIGAttributions] = None
self.start_attributions = None
self.end_attributions = None
self.input_ids: torch.Tensor = torch.Tensor()
self.position = 0
def __init__(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizer,
attribution_type: str = "lig",
):
super().__init__(model, tokenizer)
if attribution_type not in SUPPORTED_ATTRIBUTION_TYPES:
raise AttributionTypeNotSupportedError(
f"""Attribution type '{attribution_type}' is not supported.
Supported types are {SUPPORTED_ATTRIBUTION_TYPES}""")
self.attribution_type = attribution_type
self.label2id = model.config.label2id
self.id2label = model.config.id2label
self.attributions: Union[None, LIGAttributions] = None
self.input_ids: torch.Tensor = torch.Tensor()
self._single_node_output = False