bidaf_ensemble
allennlp_models.rc.models.bidaf_ensemble
BidafEnsemble#
@Model.register("bidaf-ensemble")
class BidafEnsemble(Model):
| def __init__(
| self,
| submodels: List[BidirectionalAttentionFlow]
| ) -> None
This class ensembles the output from multiple BiDAF models.
It combines results from the submodels by averaging the start and end span probabilities.
forward#
class BidafEnsemble(Model):
| ...
| @overrides
| def forward(
| self,
| question: Dict[str, torch.LongTensor],
| passage: Dict[str, torch.LongTensor],
| span_start: torch.IntTensor = None,
| span_end: torch.IntTensor = None,
| metadata: List[Dict[str, Any]] = None
| ) -> Dict[str, torch.Tensor]
The forward method runs each of the submodels, then selects the best span from the subresults. The best span is determined by averaging the probabilities for the start and end of the spans.
Parametersquestion : Dict[str, torch.LongTensor]¶
From a ``TextField``.
passage : Dict[str, torch.LongTensor]
From a TextField
. The model assumes that this passage contains the answer to the
question, and predicts the beginning and ending positions of the answer within the
passage.
span_start : torch.IntTensor
, optional
From an IndexField
. This is one of the things we are trying to predict - the
beginning position of the answer with the passage. This is an inclusive
token index.
If this is given, we will compute a loss that gets included in the output dictionary.
span_end : torch.IntTensor
, optional
From an IndexField
. This is one of the things we are trying to predict - the
ending position of the answer with the passage. This is an inclusive
token index.
If this is given, we will compute a loss that gets included in the output dictionary.
metadata : List[Dict[str, Any]]
, optional
If present, this should contain the question ID, original passage text, and token
offsets into the passage for each instance in the batch. We use this for computing
official metrics using the official SQuAD evaluation script. The length of this list
should be the batch size, and each dictionary should have the keys id
,
original_passage
, and token_offsets
. If you only want the best span string and
don't care about official metrics, you can omit the id
key.
ReturnsAn output dictionary consisting of:¶
best_span : torch.IntTensor
The result of a constrained inference over span_start_logits
and
span_end_logits
to find the most probable span. Shape is (batch_size, 2)
and each offset is a token index.
best_span_str : List[str]
If sufficient metadata was provided for the instances in the batch, we also return the
string from the original passage that the model thinks is the best answer to the
question.
get_metrics#
class BidafEnsemble(Model):
| ...
| def get_metrics(self, reset: bool = False) -> Dict[str, float]
from_params#
class BidafEnsemble(Model):
| ...
| @classmethod
| def from_params(
| cls,
| vocab: Vocabulary,
| params: Params
| ) -> "BidafEnsemble"
default_predictor#
class BidafEnsemble(Model):
| ...
| default_predictor = "reading_comprehension"
ensemble#
def ensemble(
subresults: List[Dict[str, torch.Tensor]]
) -> torch.Tensor
Identifies the best prediction given the results from the submodels.
Parameterssubresults : List[Dict[str, torch.Tensor]]¶
Results of each submodel.