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from itertools import zip_longest
import transformers.data.metrics.squad_metrics as squad_metrics
def doc_to_text(doc):
# Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
# and a question qi, the task is to predict the answer ai
doc_text = doc["story"] + "\n\n"
for q, a in zip_longest(
doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]
): # omit target answer ai
question = f"Q: {q}\n\n"
answer = f"A: {a}\n\n" if a is not None else "A:"
doc_text += question + answer
return doc_text
def doc_to_target(doc):
turn_id = len(doc["questions"]["input_text"])
# Returns unique answers and valid alternatives (Some questions in CoQA have multiple valid answers).
answers = []
answer_forturn = doc["answers"]["input_text"][turn_id - 1]
answers.append(answer_forturn)
additional_answers = doc.get("additional_answers")
if additional_answers:
for key in additional_answers:
additional_answer_for_turn = additional_answers[key]["input_text"][
turn_id - 1
]
if additional_answer_for_turn.lower() not in map(str.lower, answers):
answers.append(additional_answer_for_turn)
return answers
def em(gold_list, pred):
# tests for exact match and on the normalised answer (compute_exact)
em_sum = 0.0
if len(gold_list) > 1:
for i in range(len(gold_list)):
gold_answers = gold_list[0:i] + gold_list[i + 1 :]
# predictions compared against (n) golds and take maximum
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_answers)
else:
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
return em_sum / max(1, len(gold_list))
def compute_scores(gold_list, pred):
# tests for exact match and on the normalised answer (compute_exact)
# test for overlap (compute_f1)
f1_sum = 0.0
em_sum = 0.0
if len(gold_list) > 1:
for i in range(len(gold_list)):
gold_answers = gold_list[0:i] + gold_list[i + 1 :]
# predictions compared against (n) golds and take maximum
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_answers)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_answers)
else:
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_list)
return {
"em": em_sum / max(1, len(gold_list)),
"f1": f1_sum / max(1, len(gold_list)),
}
def process_results(doc, results):
gold_list = doc_to_target(doc)
pred = results[0].strip().split("\n")[0]
scores = compute_scores(gold_list, pred)
return scores
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