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README.md
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language_creators:
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- found
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language:
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- cs
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@@ -42,13 +44,13 @@ EU Debates is a corpus of parliamentary proceedings (debates) from the EU parlia
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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,
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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, using the EasyNMT framework with the M2M2-100 (418M) model.
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# Data Fields
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- `speaker_name: a `string` with the full
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- `speaker_party`: a `string` with the name of the euro-party (group) that the MEP is affiliated with.
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- `speaker_role`: a `string` with the role of speaker (Member of the European Parliament (MEP), EUROPARL President, etc.)
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- `debate_title`: a `string` with the title of the debate in the European Parliament.
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- `date`: a `string` with the full date (YYYY-MM-DD) of the speech.
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- `year` a `string` with the year (YYYY).
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```
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{
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'speaker_name': '
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'speaker_party': '
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'speaker_role': 'MEP',
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'debate_title': '
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'date': '
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'year': '
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'text':
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'translated_text':
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}
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```
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```python
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from datasets import load_dataset
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eu_debates_dataset = load_dataset('coastalcph/eu_debates')
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```
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<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>
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</table>
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### Citation Information
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*[Llama meets EU: Investigating the European political spectrum through the lens of LLMs.
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- extended
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language_creators:
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- found
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multilinguality:
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- multilingual
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language:
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- bg
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- cs
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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,
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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, using the EasyNMT framework with the [M2M2-100 (418M)](https://huggingface.co/facebook/m2m100_418M) model (Fan et al., 2020).
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# Data Fields
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- `speaker_name: a `string` with the full name of the speaker.
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- `speaker_party`: a `string` with the name of the euro-party (group) that the MEP is affiliated with.
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- `speaker_role`: a `string` with the role of the speaker (Member of the European Parliament (MEP), EUROPARL President, etc.)
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- `debate_title`: a `string` with the title of the debate in the European Parliament.
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- `date`: a `string` with the full date (YYYY-MM-DD) of the speech.
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- `year` a `string` with the year (YYYY).
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```
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{
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'speaker_name': 'Michèle Striffler'
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'speaker_party': 'PPE',
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'speaker_role': 'MEP',
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'debate_title': 'Famine in East Africa (debate)',
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'date': '2011-09-15',
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'year': '2011'
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'text': "Monsieur le Président, Madame le Commissaire, chers collègues, la situation humanitaire sans précédent que connaît la Corne de l'Afrique continue [...]",
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'translated_text': 'Mr. President, Mr. Commissioner, dear colleagues, the unprecedented humanitarian situation of the Horn of Africa continues [...]'}
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}
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```
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```python
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from datasets import load_dataset
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eu_debates_dataset = load_dataset('coastalcph/eu_debates', split='train')
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```
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<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>
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</table>
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Distribution of speeches across the 23 EU official languages:
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| Language | Examples |
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| ----------- | -------- |
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| en | 40736 (46.7%) |
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| de | 6497 (7.5%) |
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| fr | 6024 (6.9%) |
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| es | 5172 (5.9%) |
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| it | 4506 (5.2%) |
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| pl | 3792 (4.4%) |
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| pt | 2713 (3.1%) |
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| ro | 2308 (2.7%) |
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| el | 2290 (2.6%) |
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| nl | 2286 (2.6%) |
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| hu | 1661 (1.9%) |
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| hr | 1509 (1.7%) |
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| cs | 1428 (1.6%) |
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| sv | 1210 (1.4%) |
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| bg | 928 (1.1%) |
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| sk | 916 (1.1%) |
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| sl | 753 (0.9%) |
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| fi | 693 (0.8%) |
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| lt | 618 (0.7%) |
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| da | 578 (0.7%) |
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| et | 342 (0.4%) |
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| lv | 184 (0.2%) |
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| mt | 0 (0.0%) |
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### Citation Information
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*[Llama meets EU: Investigating the European political spectrum through the lens of LLMs.
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