Create eu_debates.py

eu_debates.py

@@ -0,0 +1,112 @@
+"""EU Debates"""
+
+import json
+import os
+import textwrap
+
+import datasets
+
+
+MAIN_CITATION = """
+@inproceedings{chalkidis-and-brandl-eu-llama-2024,
+    title = "Llama meets EU: Investigating the European political spectrum through the lens of LLMs",
+    author = "Chalkidis, Ilias  and
+      Stephanie Brandl",
+    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics",
+    month = jun,
+    year = "2021",
+    address = "Mexico City, Mexico",
+    publisher = "Association for Computational Linguistics",
+}
+"""
+
+_DESCRIPTION = """
+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. 
+We exhaustively scrape the data from the official European Parliament Plenary website. All speeches are time-stamped, thematically organized on debates, 
+and include metadata relevant to the speaker's identity (full name, euro-party affiliation, speaker role), and the debate (date and title). 
+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 
+versions in English, when official translations are missing. 
+"""
+MAIN_PATH = 'https://huggingface.co/datasets/coastalcph/eu_debates/resolve/main'
+
+
+class EUDebatesConfig(datasets.BuilderConfig):
+    """BuilderConfig for EU Debates"""
+
+    def __init__(
+        self,
+        data_url,
+        citation,
+        **kwargs,
+    ):
+        """BuilderConfig for EU Debates.
+
+        Args:
+          data_url: `string`, url to download the zip file from
+          data_file: `string`, filename for data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(EUParliamentsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.data_url = data_url
+        self.citation = citation
+
+
+class EUDebates(datasets.GeneratorBasedBuilder):
+    """N/A. Version 1.0"""
+
+    BUILDER_CONFIGS = [
+        EUParliamentsConfig(
+            name="eu",
+            data_url=os.path.join(MAIN_PATH, "eu_parliament.zip"),
+            citation=textwrap.dedent(
+                """N/A"""
+            ),
+        ),
+    ]
+
+    def _info(self):
+        features = {"text": datasets.Value("string"),
+                    "translated_text": datasets.Value("string"),
+                    "speaker_party": datasets.Value("string"),
+                    "speaker_role": datasets.Value("string"),
+                    "speaker_name": datasets.Value("string"),
+                    "debate_title": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "year": datasets.Value("string")}
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features(features),
+            homepage='https://www.europarl.europa.eu/',
+            citation=MAIN_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(self.config.data_url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, f"train.jsonl"),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                example = {
+                    "text": data["text"],
+                    "translated_text": data["translated_text"],
+                    "speaker_party": data["speaker_party"],
+                    "speaker_role": data["speaker_role"],
+                    "speaker_name": data["speaker_name"],
+                    "debate_title": data["debate_title"],
+                    "date": data["date"],
+                    "year": data["year"]
+                }
+                yield id_, example