init

README.md

@@ -1,3 +1,125 @@
 ---
 license: mit
+pretty_name: SQuAD QG
+languages: en
+multilinguality: monolingual
+size_categories: 10K<n<100K
+source_datasets: extended|wikipedia
+task_categories: question-generation
+task_ids: question-generation
 ---
+
+# Dataset Card for "qg_squad"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
+- **Size of downloaded dataset files:** 33.51 MB
+- **Size of the generated dataset:** 85.75 MB
+- **Total amount of disk used:** 119.27 MB
+
+### Dataset Summary
+[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset for question generation (QG) task. The split 
+of train/development/test set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is 
+compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11).
+
+
+### Supported Tasks and Leaderboards
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+English (en)
+
+## Dataset Structure
+### Data Instances
+#### plain_text
+- **Size of downloaded dataset files:** 33.51 MB
+- **Size of the generated dataset:** 85.75 MB
+- **Total amount of disk used:** 119.27 MB
+An example of 'train' looks as follows.
+```
+{
+  "question": "What is heresy mainly at odds with?",
+  "passage": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs." 
+             "A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit" 
+             "renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
+  "answer": "established beliefs or customs",
+  "sentence": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs .",
+  "passage_sentence": "<hl> Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs . <hl> A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
+  "passage_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl>. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.",
+  "sentence_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl> ."
+}
+```
+### Data Fields
+The data fields are the same among all splits.
+#### plain_text
+- `question`: a `string` feature.
+- `passage`: a `string` feature.
+- `answer`: a `string` feature.
+- `sentence`: a `string` feature.
+- `passage_sentence`: a `string` feature.
+- `passage_answer`: a `string` feature.
+- `sentence_answer`: a `string` feature.
+
+### Data Splits
+
+|   name   |train|validation|test |
+|----------|----:|---------:|----:|
+|plain_text|75722|     10570|11877|
+
+## Dataset Creation
+### Curation Rationale
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Source Data
+#### Initial Data Collection and Normalization
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+#### Who are the source language producers?
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Annotations
+#### Annotation process
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+#### Who are the annotators?
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Personal and Sensitive Information
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Discussion of Biases
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Other Known Limitations
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Additional Information
+### Dataset Curators
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Licensing Information
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Citation Information
+```
+TBA
+```

process.py

@@ -0,0 +1,102 @@
+""" Script to process raw SQuAD file for Question Generation format
+You need to run `python -m spacy download en_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('en_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"], 'passage': data["context"], 'answer': data["answer"]}
+
+    # get sentence
+    position = example['passage'].find(example['answer'])
+    assert position != -1
+    before_tmp = get_sentence(example['passage'][: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['passage'][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 passage_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['passage_sentence'] = re.sub(r'\s+', ' ', source_text)
+
+    # get passage_answer
+    source_text = '{0}{1} {2} {1}{3}'.format(
+        example['passage'][:position], HIGHLIGHT_TOKEN, example['answer'],
+        example['passage'][position + len(example['answer']):])
+    example['passage_answer'] = re.sub(r'\s+', ' ', source_text)
+
+    # get sentence_answer
+    before = get_sentence(example['passage'][:position])
+    if len(before) == 0 or before[-1].endswith('.'):
+        before = ''
+    else:
+        before = before[-1] if before[-1].endswith(' ') else '{} '.format(before[-1])
+    after = get_sentence(example['passage'][position + len(example['answer']):])
+    if len(after) == 0:
+        after = ''
+    else:
+        after = after[0] if after[0].startswith(' ') else ' {}'.format(after[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', 'dev': 'data/raw/dev.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_squad.py

@@ -0,0 +1,64 @@
+from datasets import load_dataset
+from datasets.tasks import Summarization
+
+_DESCRIPTION = """
+[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split 
+of test and development set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is 
+compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11).
+"""
+_URL = 'https://huggingface.co/datasets/asahi417/squad_qg/data/processed'
+_URLS = {
+    'train': ['{}/train{:02d}.jsonl'.format(_URL, i) for i in range(23)],
+    'test': ['{}/test{:02d}.jsonl'.format(_URL, i) for i in range(4)],
+    'validation': ['{}/dev{:02d}.jsonl'.format(_URL, i) for i in range(4)]
+}
+
+
+class SquadQGConfig(datasets.BuilderConfig):
+    """BuilderConfig for SquadQG"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SquadQG.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadQGConfig, self).__init__(**kwargs)
+
+
+class SquadQG(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"),
+                    "passage": datasets.Value("string"),
+                    "sentence_answer": datasets.Value("string"),
+                    "passage_answer": datasets.Value("string"),
+                    "passage_sentence": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            task_templates=[
+                Summarization(task='question generation', text_column="passage_answer", summary_column='question')
+            ],
+            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=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file["validation"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            for _id, i in enumerate(f.read().split('\n')):
+                data = json.loads(i)
+                yield _id, data