Datasets:

RJuro
/

eu_debates

@@ -1,7 +1,7 @@
 ---
 license: cc-by-nc-sa-4.0
 source_datasets:
-- extended
 language_creators:
 - found
 multilinguality:
@@ -34,133 +34,175 @@ tags:
 - politics
 size_categories:
 - 10K<n<100K
-pretty_name: EU Debates
 ---
 # Dataset Description
-EU Debates is a corpus of parliamentary proceedings (debates) from the European parliament released by [Chalkidis and Brandl (2024)](https://arxiv.org/abs/2403.13592). 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 ([Link](https://www.europarl.europa.eu/)). 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, using the EasyNMT framework with the [M2M2-100 (418M)](https://huggingface.co/facebook/m2m100_418M) model (Fan et al., 2020).
 # Data Fields
-- `speaker_name: a `string` with the full name of the speaker.
-- `speaker_party`: a `string` with the name of the euro-party (group) that the MEP is affiliated with.
-- `speaker_role`:  a `string` with the role of the speaker (Member of the European Parliament (MEP), EUROPARL President, etc.)
-- `debate_title`:  a `string` with the title of the debate in the European Parliament.
-- `date`:  a `string` with the full date (YYYY-MM-DD) of the speech.
-- `year` a `string` with the year (YYYY).
-- `text`: a `string` with the full speech of the speaker.
-- `translated_text`: a `string` with the translation of the speech in English, if the original is not.
 # Data Instances
-Example of a data instance from the EU Debates dataset:
-```
 {
-'speaker_name': 'Michèle Striffler'
-'speaker_party': 'PPE',
-'speaker_role': 'MEP',
-'debate_title': 'Famine in East Africa (debate)',
-'date': '2011-09-15',
-'year': '2011'
-'text': "Monsieur le Président, Madame le Commissaire, chers collègues, la situation humanitaire sans précédent que connaît la Corne de l'Afrique continue [...]",
-'translated_text': 'Mr. President, Mr. Commissioner, dear colleagues, the unprecedented humanitarian situation of the Horn of Africa continues [...]'}
 }
 ```
-# How to use
 ```python
 from datasets import load_dataset
-eu_debates_dataset = load_dataset('coastalcph/eu_debates', split='train')
 ```
-# Dataset Statistics
-Distribution of speeches across euro-parties:
-<table>
-<tr><td> <b>Euro-party</b> </td> <td> <b>No. of Speeches</b> </td> </tr>
-<tr><td>EPP  </td> <td> 25,455 (29%)</td> </tr>
-<tr><td>S&D </td> <td> 20,042 (23%)</td> </tr>
-<tr><td>ALDE </td> <td> 8,946 (10%)</td> </tr>
-<tr><td>ECR </td> <td> 7,493 (9%)</td> </tr>
-<tr><td>ID </td> <td> 6,970 (8%) </td> </tr>
-<tr><td>GUE/NGL </td> <td>6,780 (8%)</td> </tr>
-<tr><td>Greens/EFA </td> <td> 6,398 (7%)</td> </tr>
-<tr><td>NI </td> <td> 5,127 (6%)</td> </tr>
-<tr><td>Total </td> <td> 87,221 </td> </tr>
-</table>
-Distribution of speeches across years and euro-parties:
-<table>
-<tr><td><b>Year</b></td><td><b>EPP</b></td><td><b>S&D</b></td><td><b>ALDE</b></td><td><b>ECR</b></td><td><b>ID</b></td><td><b>GUE/NGL</b></td><td><b>Greens/EFA</b></td><td><b>NI</b></td><td><b>Total</b></td></tr>
-<tr><td> 2009 </td><td> 748 </td><td> 456 </td><td> 180 </td><td> 138 </td><td> 72 </td><td> 174 </td><td> 113 </td><td> 163 </td><td> 2044 </td></tr>
-<tr><td> 2010 </td><td> 3205 </td><td> 1623 </td><td> 616 </td><td> 340 </td><td> 341 </td><td> 529 </td><td> 427 </td><td> 546 </td><td> 7627 </td></tr>
-<tr><td> 2011 </td><td> 4479 </td><td> 2509 </td><td> 817 </td><td> 418 </td><td> 761 </td><td> 792 </td><td> 490 </td><td> 614 </td><td> 10880 </td></tr>
-<tr><td> 2012 </td><td> 3366 </td><td> 1892 </td><td> 583 </td><td> 419 </td><td> 560 </td><td> 486 </td><td> 351 </td><td> 347 </td><td> 8004 </td></tr>
-<tr><td> 2013 </td><td> 724 </td><td> 636 </td><td> 240 </td><td> 175 </td><td> 152 </td><td> 155 </td><td> 170 </td><td> 154 </td><td> 2406 </td></tr>
-<tr><td> 2014 </td><td> 578 </td><td> 555 </td><td> 184 </td><td> 180 </td><td> 131 </td><td> 160 </td><td> 144 </td><td> 180 </td><td> 2112 </td></tr>
-<tr><td> 2015 </td><td> 978 </td><td> 1029 </td><td> 337 </td><td> 405 </td><td> 398 </td><td> 325 </td><td> 246 </td><td> 240 </td><td> 3958 </td></tr>
-<tr><td> 2016 </td><td> 919 </td><td> 972 </td><td> 309 </td><td> 387 </td><td> 457 </td><td> 317 </td><td> 225 </td><td> 151 </td><td> 3737 </td></tr>
-<tr><td> 2017 </td><td> 649 </td><td> 766 </td><td> 181 </td><td> 288 </td><td> 321 </td><td> 229 </td><td> 162 </td><td> 135 </td><td> 2731 </td></tr>
-<tr><td> 2018 </td><td> 554 </td><td> 611 </td><td> 161 </td><td> 242 </td><td> 248 </td><td> 175 </td><td> 160 </td><td> 133 </td><td> 2284 </td></tr>
-<tr><td> 2019 </td><td> 1296 </td><td> 1339 </td><td> 719 </td><td> 556 </td><td> 513 </td><td> 463 </td><td> 490 </td><td> 353 </td><td> 5729 </td></tr>
-<tr><td> 2020 </td><td> 1660 </td><td> 1564 </td><td> 823 </td><td> 828 </td><td> 661 </td><td> 526 </td><td> 604 </td><td> 346 </td><td> 7012 </td></tr>
-<tr><td> 2021 </td><td> 2147 </td><td> 2189 </td><td> 1290 </td><td> 1062 </td><td> 909 </td><td> 708 </td><td> 990 </td><td> 625 </td><td> 9920 </td></tr>
-<tr><td> 2022 </td><td> 2436 </td><td> 2273 </td><td> 1466 </td><td> 1177 </td><td> 827 </td><td> 962 </td><td> 1031 </td><td> 641 </td><td> 10813 </td></tr>
-<tr><td> 2023 </td><td> 1716 </td><td> 1628 </td><td> 1040 </td><td> 878 </td><td> 619 </td><td> 779 </td><td> 795 </td><td> 499 </td><td> 7954 </td></tr>
-</table>
-Distribution of speeches across the 23 EU official languages:
-| Language    | Examples |
-| ----------- | -------- |
-| en | 40736  (46.7%) |
-| de | 6497   (7.5%) |
-| fr | 6024   (6.9%) |
-| es | 5172   (5.9%) |
-| it | 4506   (5.2%) |
-| pl | 3792   (4.4%) |
-| pt | 2713   (3.1%) |
-| ro | 2308   (2.7%) |
-| el | 2290   (2.6%) |
-| nl | 2286   (2.6%) |
-| hu | 1661   (1.9%) |
-| hr | 1509   (1.7%) |
-| cs | 1428   (1.6%) |
-| sv | 1210   (1.4%) |
-| bg | 928    (1.1%) |
-| sk | 916    (1.1%) |
-| sl | 753    (0.9%) |
-| fi | 693    (0.8%) |
-| lt | 618    (0.7%) |
-| da | 578    (0.7%) |
-| et | 342    (0.4%) |
-| lv | 184    (0.2%) |
-| mt |   0    (0.0%) |
 # Citation Information
-*[Llama meets EU: Investigating the European political spectrum through the lens of LLMs.
-Ilias Chalkidis and Stephanie Brandl.
-In the Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL),
-Mexico City, Mexico, June 16–21, 2024.](https://arxiv.org/abs/2403.13592)*
-```
 @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 Brandl, Stephanie",
@@ -170,5 +212,6 @@ Mexico City, Mexico, June 16–21, 2024.](https://arxiv.org/abs/2403.13592)*
     address = "Mexico City, Mexico",
     publisher = "Association for Computational Linguistics",
 }
-```

 ---
 license: cc-by-nc-sa-4.0
 source_datasets:
+- coastalcph/eu_debates
 language_creators:
 - found
 multilinguality:
 - politics
 size_categories:
 - 10K<n<100K
+pretty_name: EU Debates (JSONL Conversion)
 ---
 # Dataset Description
+This dataset is a **conversion of the original [`coastalcph/eu_debates`](https://huggingface.co/datasets/coastalcph/eu_debates)** dataset released by [Chalkidis and Brandl (2024)](https://arxiv.org/abs/2403.13592).
+The goal of this repository is to provide the same underlying data **without a Python loading script**, in a standard format (JSON Lines / Parquet) compatible with the current Hugging Face `datasets` library and automated data loading.
+The original EU Debates corpus consists of approx. 87k individual speeches in the period 2009–2023.
+The data was exhaustively scraped from the official European Parliament Plenary website ([link](https://www.europarl.europa.eu/)). All speeches are time-stamped, thematically organized in debates, and include metadata about:
+- the speaker's identity (full name, euro-party affiliation, speaker role),
+- the debate (date and title),
+- language information, and (where available) machine-translated versions in English.
+Older debate speeches are originally in English, while newer ones are linguistically diverse across the 23 official EU languages. Machine-translated English versions are provided using the EasyNMT framework with the [M2M-100 (418M)](https://huggingface.co/facebook/m2m100_418M) model (Fan et al., 2020).
+This repository only changes the **storage format** (to `train.jsonl` / Parquet) and **removes the Python loading script**.
+The data contents and fields are preserved from the original dataset.
 # Data Fields
+Each row / JSONL line is a single speech with the following fields:
+- `speaker_name`: `string`, full name of the speaker.
+- `speaker_party`: `string`, name of the euro-party (group) that the MEP is affiliated with.
+- `speaker_role`: `string`, role of the speaker (e.g., Member of the European Parliament (MEP), EUROPARL President).
+- `debate_title`: `string`, title of the debate in the European Parliament.
+- `date`: `string`, full date of the speech in `YYYY-MM-DD` format.
+- `year`: `string`, year of the speech in `YYYY` format.
+- `intervention_language`: `string`, language code of the original intervention.
+- `original_language`: `string`, language code of the original text.
+- `text`: `string`, full original speech of the speaker.
+- `translated_text`: `string` or `null`, machine translation of the speech into English if the original is not English, otherwise `null`.
 # Data Instances
+Example of a data instance:
+```json
 {
+  "speaker_name": "Michèle Striffler",
+  "speaker_party": "PPE",
+  "speaker_role": "MEP",
+  "debate_title": "Famine in East Africa (debate)",
+  "date": "2011-09-15",
+  "year": "2011",
+  "intervention_language": "fr",
+  "original_language": "fr",
+  "text": "Monsieur le Président, Madame le Commissaire, chers collègues, la situation humanitaire sans précédent que connaît la Corne de l'Afrique continue [...]",
+  "translated_text": "Mr. President, Mr. Commissioner, dear colleagues, the unprecedented humanitarian situation of the Horn of Africa continues [...]"
 }
 ```
+# How to Use
+### From the Hugging Face Hub
+If the dataset is hosted under `RJuro/eu_debates`:
 ```python
+from datasets import load_dataset
+eu_debates = load_dataset("RJuro/eu_debates", split="train")
+```
+### From Local Files
+If you downloaded the `train.jsonl` file locally:
+```python
 from datasets import load_dataset
+eu_debates = load_dataset(
+    "json",
+    data_files={"train": "train.jsonl"},
+    split="train",
+)
 ```
+If you use Parquet instead:
+```python
+from datasets import load_dataset
+eu_debates = load_dataset(
+    "parquet",
+    data_files={"train": "train.parquet"},
+    split="train",
+)
+```
+# Dataset Statistics
+The statistics below are inherited from the original `coastalcph/eu_debates` dataset.
+### Distribution of speeches across euro-parties:
+| Euro-party  | No. of Speeches |
+|-------------|-----------------|
+| EPP         | 25,455 (29%)    |
+| S&D         | 20,042 (23%)    |
+| ALDE        | 8,946 (10%)     |
+| ECR         | 7,493 (9%)      |
+| ID          | 6,970 (8%)      |
+| GUE/NGL     | 6,780 (8%)      |
+| Greens/EFA  | 6,398 (7%)      |
+| NI          | 5,127 (6%)      |
+| **Total**   | **87,221**      |
+### Distribution of speeches across years and euro-parties:
+| Year | EPP | S&D | ALDE | ECR | ID | GUE/NGL | Greens/EFA | NI | Total |
+|---|---|---|---|---|---|---|---|---|---|
+| 2009 | 748 | 456 | 180 | 138 | 72 | 174 | 113 | 163 | **2044** |
+| 2010 | 3205 | 1623 | 616 | 340 | 341 | 529 | 427 | 546 | **7627** |
+| 2011 | 4479 | 2509 | 817 | 418 | 761 | 792 | 490 | 614 | **10880** |
+| 2012 | 3366 | 1892 | 583 | 419 | 560 | 486 | 351 | 347 | **8004** |
+| 2013 | 724 | 636 | 240 | 175 | 152 | 155 | 170 | 154 | **2406** |
+| 2014 | 578 | 555 | 184 | 180 | 131 | 160 | 144 | 180 | **2112** |
+| 2015 | 978 | 1029 | 337 | 405 | 398 | 325 | 246 | 240 | **3958** |
+| 2016 | 919 | 972 | 309 | 387 | 457 | 317 | 225 | 151 | **3737** |
+| 2017 | 649 | 766 | 181 | 288 | 321 | 229 | 162 | 135 | **2731** |
+| 2018 | 554 | 611 | 161 | 242 | 248 | 175 | 160 | 133 | **2284** |
+| 2019 | 1296 | 1339 | 719 | 556 | 513 | 463 | 490 | 353 | **5729** |
+| 2020 | 1660 | 1564 | 823 | 828 | 661 | 526 | 604 | 346 | **7012** |
+| 2021 | 2147 | 2189 | 1290 | 1062 | 909 | 708 | 990 | 625 | **9920** |
+| 2022 | 2436 | 2273 | 1466 | 1177 | 827 | 962 | 1031 | 641 | **10813** |
+| 2023 | 1716 | 1628 | 1040 | 878 | 619 | 779 | 795 | 499 | **7954** |
+### Distribution of speeches across the 23 EU official languages:
+| Language | No. of Speeches |
+|----------|-----------------|
+| en       | 40,736 (46.7%)  |
+| de       | 6,497 (7.5%)    |
+| fr       | 6,024 (6.9%)    |
+| es       | 5,172 (5.9%)    |
+| it       | 4,506 (5.2%)    |
+| pl       | 3,792 (4.4%)    |
+| pt       | 2,713 (3.1%)    |
+| ro       | 2,308 (2.7%)    |
+| el       | 2,290 (2.6%)    |
+| nl       | 2,286 (2.6%)    |
+| hu       | 1,661 (1.9%)    |
+| hr       | 1,509 (1.7%)    |
+| cs       | 1,428 (1.6%)    |
+| sv       | 1,210 (1.4%)    |
+| bg       | 928 (1.1%)      |
+| sk       | 916 (1.1%)      |
+| sl       | 753 (0.9%)      |
+| fi       | 693 (0.8%)      |
+| lt       | 618 (0.7%)      |
+| da       | 578 (0.7%)      |
+| et       | 342 (0.4%)      |
+| lv       | 184 (0.2%)      |
+| mt       | 0 (0.0%)        |
 # Citation Information
+If you use this dataset, please cite the original work:
+> Llama meets EU: Investigating the European political spectrum through the lens of LLMs.
+> Ilias Chalkidis and Stephanie Brandl.
+> In the Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL),
+> Mexico City, Mexico, June 16–21, 2024.
+```bibtex
 @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 Brandl, Stephanie",
     address = "Mexico City, Mexico",
     publisher = "Association for Computational Linguistics",
 }
+```
+This repository only provides a format-converted, script-free version of the original dataset; all credit for data collection and annotation goes to the original authors.

eu_debates.py DELETED Viewed

@@ -1,110 +0,0 @@
-"""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
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(EUDebatesConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.data_url = data_url
-        self.citation = citation
-class EUDebates(datasets.GeneratorBasedBuilder):
-    """EU Debates. Version 1.0"""
-    BUILDER_CONFIGS = [
-        EUDebatesConfig(
-            name="eu_debates",
-            data_url=os.path.join(MAIN_PATH, "eu_debates.zip"),
-            citation=textwrap.dedent(MAIN_CITATION),
-        ),
-    ]
-    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)
-                if data['speaker_role'] == 'MEP':
-                    example = {
-                        "text": data["text"] if 'text' in data else None,
-                        "translated_text": data["translated_text"] if 'translated_text' in data else None,
-                        "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

eu_debates.zip DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:16a1d5d57ac70e4f0b86c8488674ccf571ab2eb2ef0d2d1b575152eb912229a6
-size 89476072