update

generate_reference_files.py

@@ -0,0 +1,19 @@
+import os
+from glob import glob
+from datasets import load_dataset
+
+os.makedirs('./reference_files', exist_ok=True)
+
+
+for split in ['validation', 'test']:
+    dataset = load_dataset('asahi417/qg_itquad', split=split)
+    for data in ['question', 'answer', 'sentence', 'paragraph']:
+        with open('./reference_files/{}-{}.txt'.format(data, split), 'w') as f:
+            if data == 'paragraph':
+                tmp_data = dataset['paragraph_id']
+            else:
+                tmp_data = dataset[data]
+            f.write('\n'.join([i.replace('\n', '.') for i in tmp_data]))
+
+    length = [len(open(i).read().split('\n')) for i in glob(f'reference_files/*{split}.txt')]
+    assert len(list(set(length))) == 1, length

process.py

@@ -0,0 +1,125 @@
+"""
+gsplit -l 1500 -d --additional-suffix=.jsonl test.jsonl test
+gsplit -l 1500 -d --additional-suffix=.jsonl train.jsonl train
+gsplit -l 1500 -d --additional-suffix=.jsonl validation.jsonl validation
+rm -rf test.jsonl
+rm -rf train.jsonl
+rm -rf validation.jsonl
+"""
+import json
+import os
+import re
+import spacy
+from random import seed, shuffle
+from tqdm import tqdm
+from datasets import load_dataset
+
+DATASET_NAME = "squad_it"
+DATASET_TYPES = None
+HIGHLIGHT_TOKEN = '<hl>'
+GENERATE_TEST_SPLIT = True
+SPLITTER = spacy.load('it_core_news_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)
+    if DATASET_TYPES is not None:
+        dataset = load_dataset(DATASET_NAME, DATASET_TYPES)
+    else:
+        dataset = load_dataset(DATASET_NAME)
+    for _split in dataset.keys():
+        tmp_dataset = dataset[_split]
+        with open(f'{output}/{_split}.jsonl', 'w') as f:
+            for single_data in tqdm(tmp_dataset):
+                question_str = single_data['question']
+                paragraph_str = single_data['context']
+                answer_str = single_data['answers']['text']
+                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
+                tmp_data = process_single_data(question=question_str, paragraph=paragraph_str, answer=answer_str)
+                tmp_data['paragraph_id'] = single_data['id']
+                f.write(json.dumps(tmp_data) + '\n')
+    if GENERATE_TEST_SPLIT:
+        # randomly sample for validation set
+        with open(f'{output}/train.jsonl') as f:
+            lines_train = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+        with open(f'{output}/test.jsonl') as f:
+            size = len([i for i in f.read().split('\n') if len(i) > 0])
+        paragraph_ids = list(set([i['paragraph_id'] for i in lines_train]))
+        data_train = {p: [i for i in lines_train if i['paragraph_id'] == p] for p in paragraph_ids}
+        seed(0)
+        shuffle(paragraph_ids)
+        data_test = []
+        data_train_new = []
+        for i in paragraph_ids:
+            if len(data_test) < size:
+                data_test += data_train[i]
+            else:
+                data_train_new += data_train[i]
+        with open(f'{output}/train.jsonl', 'w') as f:
+            f.write('\n'.join([json.dumps(i) for i in data_train_new]))
+        with open(f'{output}/validation.jsonl', 'w') as f:
+            f.write('\n'.join([json.dumps(i) for i in data_test]))

qg_itquad.py

@@ -0,0 +1,65 @@
+""" python -c "from datasets import load_dataset;load_dataset('.')" """
+import json
+from itertools import chain
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """[SQuAD-it](https://huggingface.co/datasets/squad_it) dataset for question generation (QG) task."""
+_URL = 'https://huggingface.co/datasets/asahi417/qg_itquad/raw/main/data/processed'
+_URLS = {
+    str(datasets.Split.TEST): [f'{_URL}/test{i:02d}.jsonl' for i in range(8)],
+    str(datasets.Split.TRAIN): [f'{_URL}/train{i:02d}.jsonl' for i in range(52)],
+    str(datasets.Split.VALIDATION): [f'{_URL}/validation{i:02d}.jsonl' for i in range(8)],
+}
+
+
+class QGITQuADConfig(datasets.BuilderConfig):
+    """BuilderConfig for SquadQG"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SquadQG.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(QGITQuADConfig, self).__init__(**kwargs)
+
+
+class QGITQuAD(datasets.GeneratorBasedBuilder):
+
+    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")
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/asahi417/lm-question-generation"
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URLS)
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.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
+