FEA-Bench / testbed /embeddings-benchmark__mteb /mteb /tasks /Classification /deu /GermanPoliticiansTwitterSentimentClassification.py
| from __future__ import annotations | |
| from mteb.abstasks.TaskMetadata import TaskMetadata | |
| from ....abstasks import AbsTaskClassification | |
| class GermanPoliticiansTwitterSentimentClassification(AbsTaskClassification): | |
| metadata = TaskMetadata( | |
| name="GermanPoliticiansTwitterSentimentClassification", | |
| description="GermanPoliticiansTwitterSentiment is a dataset of German tweets categorized with their sentiment (3 classes).", | |
| reference="https://aclanthology.org/2022.konvens-1.9", | |
| dataset={ | |
| "path": "Alienmaster/german_politicians_twitter_sentiment", | |
| "revision": "65343b17f5a76227ab2e15b9424dfab6466ffcb1", | |
| }, | |
| type="Classification", | |
| category="s2s", | |
| eval_splits=["test"], | |
| eval_langs=["deu-Latn"], | |
| main_score="accuracy", | |
| date=("2021-01-01", "2021-12-31"), | |
| form=["written"], | |
| domains=["Social", "Government"], | |
| task_subtypes=["Sentiment/Hate speech"], | |
| license="Not specified", | |
| socioeconomic_status="high", | |
| annotations_creators="human-annotated", | |
| dialect=[], | |
| text_creation="found", | |
| bibtex_citation=""" | |
| @inproceedings{schmidt-etal-2022-sentiment, | |
| title = "Sentiment Analysis on {T}witter for the Major {G}erman Parties during the 2021 {G}erman Federal Election", | |
| author = "Schmidt, Thomas and | |
| Fehle, Jakob and | |
| Weissenbacher, Maximilian and | |
| Richter, Jonathan and | |
| Gottschalk, Philipp and | |
| Wolff, Christian", | |
| editor = "Schaefer, Robin and | |
| Bai, Xiaoyu and | |
| Stede, Manfred and | |
| Zesch, Torsten", | |
| booktitle = "Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)", | |
| month = "12--15 " # sep, | |
| year = "2022", | |
| address = "Potsdam, Germany", | |
| publisher = "KONVENS 2022 Organizers", | |
| url = "https://aclanthology.org/2022.konvens-1.9", | |
| pages = "74--87", | |
| } | |
| """, | |
| n_samples={"test": 357}, | |
| avg_character_length={"test": 302.48}, | |
| ) | |
| def dataset_transform(self): | |
| self.dataset = self.dataset.rename_column("majority_sentiment", "label") | |