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README.md

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+---
+license: cc-by-sa-4.0
+pretty_name: TweetQA for question generation
+language: en
+multilinguality: monolingual
+size_categories: 1k<n<10K
+source_datasets: tweet_qa
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+tags:
+- question-generation
+---
+
+# Dataset Card for "lmqg/qag_tweetqa"
+
+
+## Dataset Description
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [TBA](TBA)
+- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
+
+### Dataset Summary
+This is the question & answer generation dataset based on the [tweet_qa](https://huggingface.co/datasets/tweet_qa). The test set of the original data is not publicly released, so we randomly sampled test questions from the training set.
+
+### Supported Tasks and Leaderboards
+* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
+  Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
+
+### Languages
+English (en)
+
+## Dataset Structure
+An example of 'train' looks as follows.
+```
+{
+  "paragraph": "I would hope that Phylicia Rashad would apologize now that @missjillscott has! You cannot discount 30 victims who come with similar stories.— JDWhitner (@JDWhitner) July 7, 2015",
+  "questions": [ "what should phylicia rashad do now?", "how many victims have come forward?" ],
+  "answers": [ "apologize", "30" ],
+  "questions_answers": "Q: what should phylicia rashad do now?, A: apologize Q: how many victims have come forward?, A: 30"
+}
+```
+The data fields are the same among all splits.
+- `questions`: a `list` of `string` features. 
+- `answers`: a `list` of `string` features.
+- `paragraph`: a `string` feature.
+- `questions_answers`: a `string` feature.
+
+## Data Splits
+
+|train|validation|test |
+|----:|---------:|----:|
+|4536 |       583|  583|
+
+
+## Citation Information
+
+```
+TBA
+```

data/processed/test.jsonl

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+size 609218

data/processed/train.jsonl

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+oid sha256:cf69758780984227312d576922a2a51fddc5c0d33fdb69783aee9566b2a7093b
+size 4749947

data/processed/validation.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d86a4926a2d826492430aa74c43150d3b448e7ee7cd825ebff95c5e9babcb8e2
+size 539321

qg_tweetqa.py

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+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_VERSION = "2.0.1"
+_NAME = "qag_tweetqa"
+_CITATION = """
+TBA
+"""
+_DESCRIPTION = """Question & answer generation dataset based on [TweetQA](https://huggingface.co/datasets/tweet_qa)."""
+_URL = "https://huggingface.co/datasets/lmqg/qag_tweetqa/resolve/main/data/processed"
+_URLS = {
+    'train': f'{_URL}/train.jsonl',
+    'test': f'{_URL}/test.jsonl',
+    'validation': f'{_URL}/validation.jsonl'
+}
+
+
+class QAGTweetQAConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(QAGTweetQAConfig, self).__init__(**kwargs)
+
+
+class QAGTweetQA(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        QAGTweetQAConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
+    ]
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "answers": datasets.Sequence(datasets.Value("string")),
+                    "questions": datasets.Sequence(datasets.Value("string")),
+                    "paragraph": datasets.Value("string"),
+                    "paragraph_id": datasets.Value("string"),
+                    "questions_answers": 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=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):
+        _key = 0
+        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