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---
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dataset_info:
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features:
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- name: score
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dtype: float32
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- name: Kikuyu
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dtype: string
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- name: Wolof
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dtype: string
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splits:
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- name: train
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num_bytes: 3816102
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num_examples: 26654
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download_size: 3816102
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dataset_size: 3816102
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configs:
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- config_name: default
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data_files:
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- split: train
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path: Kikuyu-Wolof_Sentence-Pairs.csv
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---
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# Kikuyu-Wolof_Sentence-Pairs Dataset
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This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks.
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This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php)
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## Metadata
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- **File Name**: Kikuyu-Wolof_Sentence-Pairs
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- **Number of Rows**: 26654
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- **Number of Columns**: 3
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- **Columns**: score, Kikuyu, Wolof
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## Dataset Description
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The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns:
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1. `score`: The similarity score between the two sentences (range from 0 to 1).
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2. `Kikuyu`: The first sentence in the pair (language 1).
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3. `Wolof`: The second sentence in the pair (language 2).
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This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning.
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## References
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Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications:
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[1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017
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[2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018.
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[3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018
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[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018.
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[5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018.
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[6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018.
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[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019.
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[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
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[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761.
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[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
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