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"""EU Debates""" |
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import json |
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import os |
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import textwrap |
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import datasets |
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MAIN_CITATION = """ |
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@inproceedings{chalkidis-and-brandl-eu-llama-2024, |
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title = "Llama meets EU: Investigating the European political spectrum through the lens of LLMs", |
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author = "Chalkidis, Ilias and |
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Stephanie Brandl", |
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booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics", |
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month = jun, |
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year = "2021", |
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address = "Mexico City, Mexico", |
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publisher = "Association for Computational Linguistics", |
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} |
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""" |
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_DESCRIPTION = """ |
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EU Debates is a corpus of parliamentary proceedings (debates) from the EU parliament. The corpus consists of approx. 87k individual speeches in the period 2009-2023. |
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We exhaustively scrape the data from the official European Parliament Plenary website. All speeches are time-stamped, thematically organized on debates, |
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and include metadata relevant to the speaker's identity (full name, euro-party affiliation, speaker role), and the debate (date and title). |
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Older debate speeches are originally in English, while newer ones are linguistically diverse across the 23 official EU languages, thus we also provide machine-translated |
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versions in English, when official translations are missing. |
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""" |
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MAIN_PATH = 'https://huggingface.co/datasets/coastalcph/eu_debates/resolve/main' |
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class EUDebatesConfig(datasets.BuilderConfig): |
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"""BuilderConfig for EU Debates""" |
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def __init__( |
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self, |
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data_url, |
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citation, |
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**kwargs, |
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): |
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"""BuilderConfig for EU Debates. |
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Args: |
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data_url: `string`, url to download the zip file from |
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data_file: `string`, filename for data set |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(EUDebatesConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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self.data_url = data_url |
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self.citation = citation |
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class EUDebates(datasets.GeneratorBasedBuilder): |
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"""EU Debates. Version 1.0""" |
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BUILDER_CONFIGS = [ |
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EUDebatesConfig( |
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name="eu_debates", |
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data_url=os.path.join(MAIN_PATH, "eu_debates.zip"), |
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citation=textwrap.dedent(MAIN_CITATION), |
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), |
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] |
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def _info(self): |
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features = {"text": datasets.Value("string"), |
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"translated_text": datasets.Value("string"), |
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"speaker_party": datasets.Value("string"), |
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"speaker_role": datasets.Value("string"), |
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"speaker_name": datasets.Value("string"), |
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"debate_title": datasets.Value("string"), |
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"date": datasets.Value("string"), |
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"year": datasets.Value("string")} |
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return datasets.DatasetInfo( |
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description=self.config.description, |
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features=datasets.Features(features), |
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homepage='https://www.europarl.europa.eu/', |
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citation=MAIN_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(self.config.data_url) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, f"train.jsonl"), |
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"split": "train", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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"""This function returns the examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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data = json.loads(row) |
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if data['speaker_role'] == 'MEP': |
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example = { |
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"text": data["text"] if 'text' in data else None, |
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"translated_text": data["translated_text"] if 'translated_text' in data else None, |
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"speaker_party": data["speaker_party"], |
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"speaker_role": data["speaker_role"], |
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"speaker_name": data["speaker_name"], |
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"debate_title": data["debate_title"], |
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"date": data["date"], |
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"year": data["year"] |
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} |
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yield id_, example |
