FEA-Bench / testbed /embeddings-benchmark__mteb /mteb /tasks /Classification /spa /SpanishSentimentClassification.py
| from __future__ import annotations | |
| from mteb.abstasks import AbsTaskClassification | |
| from mteb.abstasks.TaskMetadata import TaskMetadata | |
| class SpanishSentimentClassification(AbsTaskClassification): | |
| metadata = TaskMetadata( | |
| name="SpanishSentimentClassification", | |
| description="A Spanish dataset for sentiment classification.", | |
| reference="https://huggingface.co/datasets/sepidmnorozy/Spanish_sentiment", | |
| dataset={ | |
| "path": "sepidmnorozy/Spanish_sentiment", | |
| "revision": "2a6e340e4b59b7c0a78c03a0b79ac27e1b4a2662", | |
| }, | |
| type="Classification", | |
| category="s2s", | |
| date=("2022-08-16", "2022-08-16"), | |
| eval_splits=["validation", "test"], | |
| eval_langs=["spa-Latn"], | |
| main_score="accuracy", | |
| form=["written"], | |
| domains=["Reviews"], | |
| task_subtypes=["Sentiment/Hate speech"], | |
| license="Not specified", | |
| socioeconomic_status="mixed", | |
| annotations_creators="derived", | |
| dialect=[], | |
| text_creation="found", | |
| bibtex_citation=""" | |
| @inproceedings{mollanorozy-etal-2023-cross, | |
| title = "Cross-lingual Transfer Learning with \{P\}ersian", | |
| author = "Mollanorozy, Sepideh and | |
| Tanti, Marc and | |
| Nissim, Malvina", | |
| editor = "Beinborn, Lisa and | |
| Goswami, Koustava and | |
| Murado{\\u{g}}lu, Saliha and | |
| Sorokin, Alexey and | |
| Kumar, Ritesh and | |
| Shcherbakov, Andreas and | |
| Ponti, Edoardo M. and | |
| Cotterell, Ryan and | |
| Vylomova, Ekaterina", | |
| booktitle = "Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP", | |
| month = may, | |
| year = "2023", | |
| address = "Dubrovnik, Croatia", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2023.sigtyp-1.9", | |
| doi = "10.18653/v1/2023.sigtyp-1.9", | |
| pages = "89--95", | |
| } | |
| """, | |
| n_samples={"validation": 147, "test": 296}, | |
| avg_character_length={"validation": 85.02, "test": 87.91}, | |
| ) | |