update

process.py

@@ -0,0 +1,104 @@
+""" Script to process raw SQuADshift file for Question Generation format
+cd data/processed
+gsplit -b 6M -d --additional-suffix=.jsonl new_wiki.test.jsonl new_wiki.test
+gsplit -b 6M -d --additional-suffix=.jsonl nyt.test.jsonl nyt.test
+gsplit -b 6M -d --additional-suffix=.jsonl reddit.test.jsonl reddit.test
+gsplit -b 6M -d --additional-suffix=.jsonl amazon.test.jsonl amazon.test
+
+rm -rf new_wiki.test.jsonl
+rm -rf nyt.test.jsonl
+rm -rf reddit.test.jsonl
+rm -rf amazon.test.jsonl
+"""
+import json
+import os
+import re
+import spacy
+
+from tqdm import tqdm
+from datasets import load_dataset
+
+DATASET_NAME = "squadshifts"
+DATASET_TYPES = ['new_wiki', 'nyt', 'reddit', 'amazon']
+HIGHLIGHT_TOKEN = '<hl>'
+SPLITTER = spacy.load('en_core_web_sm')
+
+
+def get_sentence(document: str): return [str(sent) for sent in SPLITTER(document).sents]
+
+
+def process_single_data(question: str, paragraph: str, answer: str):
+    """ Convert single raw json data into QG format """
+    example = {'question': question, 'paragraph': paragraph, 'answer': answer}
+    start = example['paragraph'].find(example['answer'])
+    end = start + len(answer)
+    assert paragraph[start:end] == answer
+    # get sentence
+    before_tmp = get_sentence(example['paragraph'][:start])
+    if len(before_tmp) == 0:
+        before = ''
+        before_sentence = ''
+    else:
+        if before_tmp[-1].endswith('.'):
+            before = ' '.join(before_tmp)
+            before_sentence = ''
+        else:
+            before = ' '.join(before_tmp[:-1])
+            before_sentence = before_tmp[-1]
+            before_sentence = before_sentence if before_sentence.endswith(' ') else f'{before_sentence} '
+    after_tmp = get_sentence(example['paragraph'][start + len(example['answer']):])
+    if len(after_tmp) == 0:
+        after = ''
+        after_sentence = ''
+    else:
+        after = ' '.join(after_tmp[1:])
+        after_sentence = after_tmp[0]
+        after_sentence = after_sentence if after_sentence.startswith(' ') else f' {after_sentence}'
+    example['sentence'] = f"{before_sentence}{example['answer']}{after_sentence}"
+
+    # get paragraph_sentence
+    before = '' if before == '' else f'{before} '
+    after = '' if after == '' else f' {after}'
+    source_text = '{0}{1} {2} {1}{3}'.format(before, HIGHLIGHT_TOKEN, example['sentence'], after)
+    example['paragraph_sentence'] = re.sub(r'\s+', ' ', source_text)
+
+    # get paragraph_answer
+    source_text = '{0}{1} {2} {1}{3}'.format(
+        example['paragraph'][:start], HIGHLIGHT_TOKEN, example['answer'],
+        example['paragraph'][start + len(example['answer']):])
+    example['paragraph_answer'] = re.sub(r'\s+', ' ', source_text)
+
+    # get sentence_answer
+    if len(before_tmp) == 0 or before_tmp[-1].endswith('.'):
+        before = ''
+    else:
+        before = before_tmp[-1] if before_tmp[-1].endswith(' ') else f'{before_tmp[-1]} '
+    if len(after_tmp) == 0:
+        after = ''
+    else:
+        after = after_tmp[0] if after_tmp[0].startswith(' ') else f' {after_tmp[0]}'
+    source_text = '{0}{1} {2} {1}{3}'.format(before, HIGHLIGHT_TOKEN, example['answer'], after)
+    example['sentence_answer'] = re.sub(r'\s+', ' ', source_text)
+
+    return example
+
+
+if __name__ == '__main__':
+    output = './data/processed'
+    os.makedirs(output, exist_ok=True)
+    for data_type in DATASET_TYPES:
+        dataset = load_dataset(DATASET_NAME, data_type)
+        for _split in dataset.keys():
+            tmp_dataset = dataset[_split]
+            with open(f'{output}/{data_type}.{_split}.jsonl', 'w') as f:
+                for single_data in tqdm(tmp_dataset):
+                    answer_str = single_data['answers']['text']
+                    question_str = single_data['question']
+                    paragraph_str = single_data['context']
+                    if type(answer_str) == list:
+                        answer_str = answer_str[0]
+                    assert type(answer_str) is str, answer_str
+                    assert type(question_str) is str, question_str
+                    assert type(paragraph_str) is str, paragraph_str
+                    single_data = process_single_data(question=question_str, paragraph=paragraph_str, answer=answer_str)
+                    f.write(json.dumps(single_data) + '\n')

qg_squadshift.py

@@ -0,0 +1,79 @@
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """[SQuAD Shifts](https://modestyachts.github.io/squadshifts-website/index.html) dataset for question generation (QG) task."""
+_URL = 'https://huggingface.co/datasets/asahi417/qg_squadshift/raw/main/data/processed'
+_DOMAINS = ['new_wiki', 'nyt', 'reddit', 'amazon']
+_FILESIZE = [4, 5, 5, 5]
+
+
+class QGSQuADShiftsConfig(datasets.BuilderConfig):
+    """BuilderConfig for SquadQG"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SquadQG.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(QGSQuADShiftsConfig, self).__init__(**kwargs)
+
+
+class QGSQuADShifts(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [QGSQuADShiftsConfig(name="default", description="All domain.")]
+    BUILDER_CONFIGS += [QGSQuADShiftsConfig(name=i, description=i) for i in _DOMAINS]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "answer": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "paragraph": datasets.Value("string"),
+                    "sentence_answer": datasets.Value("string"),
+                    "paragraph_answer": datasets.Value("string"),
+                    "paragraph_sentence": datasets.Value("string"),
+                    "paragraph_id": datasets.Value("string"),
+                    "question_subj_level": datasets.Value("int32"),
+                    "answer_subj_level": datasets.Value("int32"),
+                    "domain": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/asahi417/lm-question-generation"
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name == 'default':
+            downloaded_file = dl_manager.download_and_extract({
+                'train': [f"{_URL}/{i}.train.jsonl" for i in _DOMAINS],
+                'dev': [f"{_URL}/{i}.dev.jsonl" for i in _DOMAINS],
+                'test': [f"{_URL}/{i}.test.jsonl" for i in _DOMAINS]
+            })
+        else:
+            downloaded_file = dl_manager.download_and_extract({
+                'train': [f"{_URL}/{self.config.name}.train.jsonl"],
+                'dev': [f"{_URL}/{self.config.name}.dev.jsonl"],
+                'test': [f"{_URL}/{self.config.name}.test.jsonl"]
+            })
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_file["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": downloaded_file["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": downloaded_file["test"]})
+        ]
+
+    def _generate_examples(self, filepaths):
+        _key = 0
+        for filepath in filepaths:
+            logger.info("generating examples from = %s", filepath)
+            with open(filepath, encoding="utf-8") as f:
+                _list = f.read().split('\n')
+                if _list[-1] == '':
+                    _list = _list[:-1]
+                for i in _list:
+                    data = json.loads(i)
+                    yield _key, data
+                    _key += 1