192 lines
8.1 KiB
Python
Executable File
192 lines
8.1 KiB
Python
Executable File
import sys
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sys.path.append('../data_process')
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from typing import List, Optional, Tuple
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from QAInput import StructuralQAInput as QAInput
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#from QAInput import QAInputNoPrompt as QAInput
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def preprocess_sqaud_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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questions = examples[question_column]
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contexts = examples[context_column]
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answers = examples[answer_column]
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inputs = [QAInput.qg_input_extractive_qa(context, question) for question, context in zip(questions, contexts)]
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targets = [answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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return inputs, targets
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def preprocess_sqaud_abstractive_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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questions = examples[question_column]
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contexts = examples[context_column]
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answers = examples[answer_column]
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inputs = [QAInput.qg_input_abstrativeqa(context, question) for question, context in zip(questions, contexts)]
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targets = [answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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return inputs, targets
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def preprocess_boolq_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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question_column, context_column, answer_column = 'question', 'passage', 'answer'
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questions = examples[question_column]
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contexts = examples[context_column]
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answers = examples[answer_column]
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inputs = [QAInput.qg_input_boolqa(context, question) for question, context in zip(questions, contexts)]
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targets = [str(ans) for ans in answers] #[answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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# print(inputs,targets)
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return inputs, targets
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def preprocess_boolq_batch_pretrain(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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questions = examples[question_column]
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contexts = examples[context_column]
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answers = examples[answer_column]
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inputs = [QAInput.qg_input_boolqa(context, question) for question, context in zip(questions, contexts)]
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targets = [answer["text"][0].capitalize() if len(answer["text"]) > 0 else "" for answer in answers]
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# print(inputs,targets)
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return inputs, targets
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def preprocess_narrativeqa_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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contexts = [exp['summary']['text'] for exp in examples['document']]
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questions = [exp['text'] for exp in examples['question']]
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answers = [ans[0]['text'] for ans in examples['answers']]
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inputs = [QAInput.qg_input_abstrativeqa(context, question) for question, context in zip(questions, contexts)]
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targets = answers #[answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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return inputs, targets
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def preprocess_narrativeqa_batch_pretrain(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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questions = examples[question_column]
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contexts = examples[context_column]
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answers = examples[answer_column]
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inputs = [QAInput.qg_input_abstrativeqa(context, question) for question, context in zip(questions, contexts)]
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targets = [answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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return inputs, targets
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def preprocess_drop_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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contexts = examples['passage']
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questions = examples['question']
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answers = examples['answers']
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inputs = [QAInput.qg_input_abstrativeqa(context, question) for question, context in zip(questions, contexts)]
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targets = [answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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return inputs, targets
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def preprocess_race_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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contexts = examples['article']
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questions = examples['question']
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all_options = examples['options']
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answers = examples['answer']
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options_texts = [f'options: A. {options[0]}; B. {options[1]}; C. {options[2]}; D. {options[3]}' for options in all_options]
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inputs = [QAInput.qg_input_multirc(context, question, ops) for question, context, ops in zip(questions, contexts, options_texts)]
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ans_map = {'A': 0, 'B': 1, 'C': 2, 'D': 3 }
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targets = [options[ans_map[answer]] for options, answer in zip(all_options, answers)]
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return inputs, targets
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def preprocess_newsqa_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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questions = examples[question_column]
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contexts = examples[context_column]
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answers = examples[answer_column]
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inputs = [QAInput.qg_input_extractive_qa(context, question) for question, context in zip(questions, contexts)]
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# inputs = [QAInput.qg_input_abstrativeqa(context, question) for question, context in zip(questions, contexts)]
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targets = [answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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return inputs, targets
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def preprocess_ropes_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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questions = examples[question_column]
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backgrounds = examples["background"]
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situations = examples["situation"]
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answers = examples[answer_column]
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inputs = [QAInput.qg_input_extractive_qa(" ".join([background, situation]), question) for question, background, situation in zip(questions, backgrounds, situations)]
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targets = [answer["text"][0] if len(answer["text"]) > 0 else "" for answer in answers]
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return inputs, targets
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def preprocess_openbookqa_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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questions = examples['question_stem']
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all_options = examples['choices']
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answers = examples['answerKey']
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options_texts = [f"options: A. {options['text'][0]}; B. {options['text'][1]}; C. {options['text'][2]}; D. {options['text'][3]}" for options in all_options]
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inputs = [QAInput.qg_input_multirc("", question, ops) for question, ops in zip(questions, options_texts)]
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ans_map = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
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targets = [options['text'][ans_map[answer]] for options, answer in zip(all_options, answers)]
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return inputs, targets
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def preprocess_social_iqa_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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contexts = examples['article']
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questions = examples['question']
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all_options = examples['options']
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answers = examples['answer']
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options_texts = [f'options: A. {options[0]}; B. {options[1]}; C. {options[2]}' for options in all_options]
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inputs = [QAInput.qg_input_multirc(context, question, ops) for question, context, ops in zip(questions, contexts, options_texts)]
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ans_map = {'A': 0, 'B': 1, 'C': 2,}
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targets = [options[ans_map[answer]] for options, answer in zip(all_options, answers)]
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return inputs, targets
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def preprocess_dream_batch(
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examples,
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question_column: str,
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context_column: str,
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answer_column: str,
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) -> Tuple[List[str], List[str]]:
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contexts = [" ".join(dialogue) for dialogue in examples['dialogue']]
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questions = examples['question']
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all_options = examples['choice']
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answers = examples['answer']
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answer_idxs = [options.index(answer) for answer, options in zip(answers, all_options)]
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options_texts = [f'options: A. {options[0]}; B. {options[1]}; C. {options[2]}' for options in all_options]
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inputs = [QAInput.qg_input_multirc(context, question, ops) for question, context, ops in zip(questions, contexts, options_texts)]
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targets = answers
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return inputs, targets |