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--- |
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dataset_info: |
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features: |
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- name: text_bem |
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dtype: string |
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- name: text_en |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2041746 |
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num_examples: 20121 |
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download_size: 1203727 |
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dataset_size: 2041746 |
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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: data/train-* |
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task_categories: |
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- translation |
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language: |
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- af |
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- en |
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--- |
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# Dataset Details |
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This dataset contains Bemba-to-English sentences which is intended to machine translation task. This dataset was gained from [Tatoeba-Translations](https://huggingface.co/datasets/ymoslem/Tatoeba-Translations) repository by Yasmin Moslem. |
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Tatoeba is a dataset of sentences and its translations [1]. |
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# Preprocessing Notes |
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There are several preprocessing processes done with this dataset. |
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1. Take for about ~300k English data from Tatoeba. |
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2. Translating that English data using our [MT model](https://huggingface.co/kreasof-ai/nllb-200-600M-eng2bem) and keep the translating score. |
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3. Keep the data that has score above 0,7. |
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4. Add `<bt>` tag in front of the Bemba sentences to indicate that the data is generated by back-translating method. |
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# Dataset Structure |
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``` |
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DatasetDict({ |
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train: Dataset({ |
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features: ['text_bem', 'text_en'], |
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num_rows: 20121 |
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}) |
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}) |
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``` |
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# Reference |
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``` |
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1. https://tatoeba.org/ |
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``` |
