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"""
Creates frozen dataclass objects per individual ground-truth example and individual prediction.
Benefits:
- Instance immutability: avoids accidental changes to data which would be otherwise unexpected
- Explicit type annotation across object fields, removes ambiguity
- Compact implementation: reduces boilerplate code (e.g., __init__() is auto-generated)
- Post-init preserves consistent validation for each and every object created
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import List, Dict
@dataclass(frozen=True)
class QAExample:
"""
Single QA instance pulled from SQuAD (gold/ground-truth instance) as a
frozen dataclass to preserve immutability throughout the code's execution.
As per the official evaluation script, storing all possible gold answers.
If is_impossible is True then answer_texts and answer_starts are expected to be empty;
this is guaranteed during __post_init__().
"""
question_id: str
title: str
question: str
context: str
answer_texts: List[str] # empty list when is_impossible is True
answer_starts: List[int] # empty list when is_impossible is True
is_impossible: bool
def __post_init__(self):
if not isinstance(self.is_impossible, bool):
raise ValueError("is_impossible field needs to be of boolean type.")
if len(self.answer_texts) != len(self.answer_starts):
raise ValueError(
"Incompatible sizes of answer_texts/answer_starts of QAExample."
)
if self.is_impossible:
if self.answer_texts or self.answer_starts:
raise ValueError(
"Incompatible configuration between is_impossible (True) Vs answer_texts/answer_starts (non-empty) of QAExample."
)
else:
if not self.answer_texts or not self.answer_starts:
raise ValueError(
"Incompatible configuration between is_impossible (False) Vs answer_texts/answer_starts (empty) of QAExample."
)
@dataclass(frozen=True)
class Prediction:
"""
Single model prediction for a question.
__post_init__() method validates for consistency with expected values.
"""
question_id: str
predicted_answer: str # '' if the model predicts no-answer
confidence: float # corresponds to the confidence level that the question is answerable via the context
is_impossible: bool
def __post_init__(self):
if not (0 <= self.confidence <= 1):
raise ValueError(
"Confidence of Prediction object should be a probability score [0, 1]."
)
@classmethod
def null(cls, question_id: str, confidence: float = 0.0) -> Prediction:
"""
No-answer Prediction constructor to standardize it throughout the code.
"""
return cls(
question_id=question_id,
predicted_answer="",
confidence=confidence,
is_impossible=True,
)
@classmethod
def flatten_predicted_answers(
cls, predictions: Dict[str, Prediction]
) -> Dict[str, str]:
"""
Convert Dict[qid, Prediction] -> Dict[qid, str] -
similar to official evaluation script style.
"""
# TODO - add an extra check that each key of the Dict matches with the
# question ID stored as part of the Prediction object
return {qid: p.predicted_answer for qid, p in predictions.items()}
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