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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:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
-- **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
-
-```
-@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",
-}
-```

process.py

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-import json
-import os
-from random import seed, shuffle
-import re
-from tqdm import tqdm
-from typing import Dict
-from datasets import load_dataset
-
-
-SEP_TOKEN = " | "
-
-
-def create_data(hf_data):
-    df = hf_data.to_pandas()
-    output = []
-    for tweet, g in df.groupby("Tweet"):
-        example = {
-            'paragraph': tweet.replace(SEP_TOKEN, " "),
-            "paragraph_id": '-'.join(g['qid']),
-            'questions': [_g.replace(SEP_TOKEN, " ") for _g in g['Question']],
-            'answers': [_g[0].replace(SEP_TOKEN, " ") for _g in g['Answer']],
-        }
-        example["questions_answers"] = SEP_TOKEN.join([f"question: {q}, answer: {a}" for q, a in zip(example["questions"], example["answers"])])
-        output.append(example)
-    return output
-
-
-if __name__ == '__main__':
-    tweet_qa = load_dataset("tweet_qa")
-    data_valid = create_data(tweet_qa['validation'])
-    data_train = create_data(tweet_qa['train'])
-    seed(1)
-    test_len = len(data_valid)
-    shuffle(data_train)
-    data_test = data_train[:test_len]
-    data_train = data_train[test_len:]
-    data_all = {'train': data_train, 'validation': data_valid, 'test': data_test}
-    output = './data/processed'
-    os.makedirs(output, exist_ok=True)
-    for k, _data in data_all.items():
-        with open('{}/{}.jsonl'.format(output, k), 'w') as f:
-            for single_data in tqdm(_data):
-                f.write(json.dumps(single_data) + '\n')

qag_tweetqa.py

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-import json
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-_VERSION = "2.0.1"
-_NAME = "qag_tweetqa"
-_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 = """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

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