init

.gitattributes

@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data/processed/test.jsonl filter=lfs diff=lfs merge=lfs -text
+data/processed/train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/processed/validation.jsonl filter=lfs diff=lfs merge=lfs -text

data/processed/test.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c97be8ad690988b11ae238bdbb26da763b28334ab6894185337037fa69b2c0d
+size 38396960

data/processed/train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8debcac1fbd7bc09cad41e51fa2a87b77cb69723460a70fbc167bb4431a82427
+size 279350289

data/processed/validation.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04ba1bbd2cc7af963535d98dbbb86ed44f99748355f1243c3ff9b4c8fc7dc3d8
+size 39458227

format_squad.py

@@ -0,0 +1,42 @@
+import json
+import os
+from random import shuffle, seed
+
+
+def get_dict(filepath):
+    output = []
+    with open(filepath) as f:
+        tmp = json.load(f)["data"]
+        for t in tmp:
+            for x in t["paragraphs"]:
+                context = x["context"]
+                for qa in x["qas"]:
+                    if qa["is_impossible"]:
+                        continue
+                    answers = qa["answers"]
+                    if len(answers) == 0:
+                        continue
+                    answer = answers[0]["text"]
+                    if answer not in context:
+                        continue
+                    output.append({
+                        "answer": answer,
+                        "context": context,
+                        "question": qa["question"]
+                    })
+    return output
+
+
+
+train = get_dict("dataset/train-zen-v1.0.json")
+dev = get_dict("dataset/dev-zen-v1.0.json")
+seed(42)
+shuffle(train)
+test = train[:len(dev)]
+train = train[len(dev):]
+with open("data/raw.train.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(x) for x in train]))
+with open("data/raw.valid.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(x) for x in dev]))
+with open("data/raw.test.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(x) for x in test]))

process.py

@@ -0,0 +1,100 @@
+""" Script to process raw SQuAD file for Question Generation format
+You need to run `python -m spacy download zh_core_web_sm`.
+Split when uploading to dataset hub by
+```
+gsplit -l 3300 -d --additional-suffix=.jsonl train.jsonl train
+gsplit -l 3300 -d --additional-suffix=.jsonl test.jsonl test
+gsplit -l 3300 -d --additional-suffix=.jsonl dev.jsonl dev
+```
+"""
+import json
+import os
+import re
+from glob import glob
+from tqdm import tqdm
+from typing import List, Dict
+
+import spacy
+
+SPLITTER = spacy.load('zh_core_web_sm')
+HIGHLIGHT_TOKEN = '<hl>'
+
+
+def get_sentence(document: str):
+    return [str(s) for s in SPLITTER(document).sents]
+
+
+def jsonline_reader(filename: str):
+    with open(filename, 'r') as f_reader:
+        examples = [json.loads(i) for i in f_reader.read().split('\n') if len(i) > 0]
+    return examples
+
+
+def process_single_data(data: Dict):
+    """ Convert single raw json data into QG format """
+    example = {'question': data["question"], 'paragraph': data["context"], 'answer': data["answer"]}
+
+    # get sentence
+    position = example['paragraph'].find(example['answer'])
+    assert position != -1
+    before_tmp = get_sentence(example['paragraph'][:position])
+    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 '{} '.format(before_sentence)
+    after_tmp = get_sentence(example['paragraph'][position + 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 ' {}'.format(after_sentence)
+    example['sentence'] = '{}{}{}'.format(before_sentence, example['answer'], after_sentence)
+
+    # get paragraph_sentence
+    before = '' if before == '' else '{} '.format(before)
+    after = '' if after == '' else ' {}'.format(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'][:position], HIGHLIGHT_TOKEN, example['answer'],
+        example['paragraph'][position + 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 '{} '.format(before_tmp[-1])
+    if len(after_tmp) == 0:
+        after = ''
+    else:
+        after = after_tmp[0] if after_tmp[0].startswith(' ') else ' {}'.format(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)
+    path = {'train': 'data/raw.train.jsonl', 'validation': 'data/raw.valid.jsonl', 'test': 'data/raw.test.jsonl'}
+    for k, v in path.items():
+        json_data = []
+        for _file in sorted(glob(v)):
+            json_data += jsonline_reader(_file)
+        with open('{}/{}.jsonl'.format(output, k), 'w') as f:
+            for single_data in tqdm(json_data):
+                single_data = process_single_data(single_data)
+                f.write(json.dumps(single_data) + '\n')

qg_zhquad.py

@@ -0,0 +1,83 @@
+""" python -c "from datasets import load_dataset;load_dataset('.')" """
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_VERSION = "0.0.0"
+_NAME = "qg_zhquad"
+_CITATION = """
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+"""
+_DESCRIPTION = """[Chinese SQuAD](https://github.com/junzeng-pluto/ChineseSquad) dataset for question generation (QG) task."""
+_URL = 'https://huggingface.co/datasets/lmqg/qg_zhquad/resolve/main/data/processed'
+_URLS = {
+    str(datasets.Split.TEST): [f'{_URL}/test.jsonl'],
+    str(datasets.Split.TRAIN): [f'{_URL}/train.jsonl'],
+    str(datasets.Split.VALIDATION): [f'{_URL}/validation.jsonl'],
+}
+
+
+class QGZHQuADConfig(datasets.BuilderConfig):
+    """BuilderConfig for SquadQG"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SquadQG.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(QGZHQuADConfig, self).__init__(**kwargs)
+
+
+class QGZHQuAD(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        QGZHQuADConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "answer": datasets.Value("string"),
+                    "paragraph_question": 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