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from typing import Optional, Tuple
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import torch
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from dataclasses import dataclass
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from transformers.file_utils import ModelOutput
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from transformers.modeling_outputs import QuestionAnsweringModelOutput
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@dataclass
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class QuestionAnsweringNaModelOutput(ModelOutput):
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"""
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Base class for outputs of question answering models.
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Args:
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loss (:obj:`torch.FloatTensor`, `optional`):
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Loss of the output.
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start_logits (:obj:`torch.FloatTensor`):
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Span start logits.
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end_logits (:obj:`torch.FloatTensor`):
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Span end logits.
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has_logits (:obj:`torch.FloatTensor`):
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Has logits tensor.
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hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`):
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Hidden states of the model at the output of each layer plus the initial embedding outputs.
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attentions (:obj:`tuple(torch.FloatTensor)`, `optional`):
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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loss: Optional[torch.FloatTensor] = None
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start_logits: torch.FloatTensor = None
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end_logits: torch.FloatTensor = None
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has_logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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attentions: Optional[Tuple[torch.FloatTensor]] = None
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