--- dataset_info: - config_name: aya_human_annotated features: - name: id dtype: int64 - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: script dtype: string splits: - name: test num_bytes: 84266 num_examples: 250 download_size: 62478 dataset_size: 84266 - config_name: dolly_machine_translated features: - name: id dtype: int64 - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: script dtype: string splits: - name: test num_bytes: 299665 num_examples: 400 download_size: 211317 dataset_size: 299665 configs: - config_name: aya_human_annotated data_files: - split: test path: aya_human_annotated/test-* - config_name: dolly_machine_translated data_files: - split: test path: dolly_machine_translated/test-* license: apache-2.0 task_categories: - question-answering - translation - summarization - zero-shot-classification language: - zh pretty_name: Heng666/Traditional_Chinese-aya_evaluation_suite size_categories: - 1M ## 資料集描述 **繁體中文 Aya (Traditional Chinese Aya Chinese;TCA)：專注於繁體中文處理的 Aya 集合的精選子集** ### 概述 `繁體中文 Aya` 是一個精心策劃的資料集，源自 [CohereForAI](https://huggingface.co/CohereForAI) 的綜合 Aya 集合，特別關注繁體中文文本資料。 此資料集結合了來自 [CohereForAI/aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite)，過濾掉除繁體中文、簡體中文內容之外的所有內容。 ### 目標 `繁體中文 Aya` 的目標是為研究人員、技術專家和語言學家提供即用型繁體中文文本資源，顯著減少專注於繁體中文的 NLP 和 AI 專案中數據預處理所需的時間和精力。 ### 資料集來源與資訊 - **資料來源**: 從 [CohereForAI/aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite) 3 個子集而來。 - **語言**: 繁體中文、簡體中文（'zho') - **應用**: 非常適合語言建模、文本分類、情感分析、和機器翻譯等任務。 - **論文連結:** [2402.06619](https://huggingface.co/papers/2402.06619) - **維護人:** [Heng666](https://huggingface.co/Heng666) - **License:** Apache-2.0 ### 使用方法 此資料集是開始繁體中文語言專案（從學術研究到商業應用）的基礎工具。 透過提供預先過濾的繁體中文文本來源，`繁體中文 Aya` 讓研究人員、技術專家和開發人員能夠直接進行模型訓練、分析和應用程式開發，而無需進行資料清理和語言過濾的初步麻煩。 展示範例 ```python from datasets import load_dataset dataset = load_dataset("Heng666/Traditional_Chinese-aya_evaluation_suite", "aya_human_annotated") ``` 在上面的程式碼片段中，「aya_dataset」指的是原始 「aya_evaluation_suite」中「aya_human_annotated」子集的繁體中文版本。 您可以透過在載入資料集時指定其名稱來載入其他子集。 ### 訪問和貢獻 可在 [Heng666/Traditional_Chinese-aya_evaluation_suite](https://huggingface.co/datasets/Heng666/Traditional_Chinese-aya_evaluation_suite) 下的 Hugging Face Hub 上獲取， `繁體中文 Aya` 邀請社區做出貢獻。鼓勵用戶提供回饋、提出改進建議。 ### 支持與合作 我們致力於圍繞繁體中文人工智慧和 NLP 研究創造一個包容和支持的環境。如需支援、協作或有關資料集的疑問，請透過 Hugging Face Hub 的討論部分進行聯絡。 # Original Dataset Card of Aya by CohereForAI --- ![Aya Header](https://huggingface.co/datasets/CohereForAI/aya_dataset/resolve/main/aya_header.png) # Dataset Summary `Aya Evaluation Suite` contains a total of 26,750 open-ended conversation-style prompts to evaluate multilingual open-ended generation quality.\ To strike a balance between language coverage and the quality that comes with human curation, we create an evaluation suite that includes: 1) human-curated examples in 7 languages (`tur, eng, yor, arb, zho, por, tel`) → `aya-human-annotated`. 2) machine-translations of handpicked examples into 101 languages → `dolly-machine-translated`. 3) human-post-edited translations into 6 languages (`hin, srp, rus, fra, arb, spa`) → `dolly-human-edited`. --- - **Curated by:** Contributors of [Aya Open Science Intiative](https://aya.for.ai/), professional annotators, and synthetic generation - **Language(s):** 101 languages - **License:** [Apache 2.0](https://opensource.org/license/apache-2-0) - **Aya Datasets Family:** | Name | Explanation | |------|--------------| | [aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) | Human-annotated multilingual instruction finetuning dataset, comprising over 204K instances across 65 languages. | | [aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection) | Created by applying instruction-style templates from fluent speakers to 44 datasets, including translations of 19 instruction-style datasets into 101 languages, providing 513M instances for various tasks.| | [aya_evaluation_suite](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite) | A diverse evaluation set for multilingual open-ended generation, featuring 250 culturally grounded prompts in 7 languages, 200 translated prompts in 24 languages, and human-edited versions selected for cross-cultural relevance from English Dolly in 6 languages.| # Dataset The `Aya Evaluation Suite` includes the following subsets: 1. **aya-human-annotated**: 250 original human-written prompts in 7 languages each. 2. **dolly-machine-translated**: 200 human-selected prompts from [databricks-dolly-15k](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm) , automatically translated with the [NLLB model](https://ai.meta.com/research/no-language-left-behind/) from English into 101 languages (114 dialects in total). 3. **dolly-human-edited**: 200 dolly-machine-translated prompts post-edited by fluent speakers for 6 languages. ## Load with Datasets To load this dataset consisting of prompt-completions with `datasets`, you just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset aya_eval = load_dataset("CohereForAI/aya_evaluation_suite", "dataset") ``` ## Data Fields - `id`: Unique id of the data point. - `inputs`: Prompt or input to the language model. - `targets`: Completion or output of the language model. (Not applicable for `dolly-human-edited`) - `language`: The language of the `prompt` and `completion.` - `script`: The writing system of the language. - `source_id`: Corresponding original row index from the [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset (Field applicable only for subsets `dolly-machine-translated` & `dolly-human-edited`) ## Data Instances Example data instances from the `Aya Evaluation Suite` subsets are listed in the toggled sections below.
aya-human-annotated ```json { "id": 42, "inputs": "What day is known as Star Wars Day?", "targets": "May 4th (May the 4th be with you!)", "language": "eng", "script": "Latn", } ```
Dolly-machine-translated and dolly-human-edited - These two subsets are parallel datasets (data instances can be mapped using their `id` column). - Note that in the `dolly-machine-translated` subset, we also include the original English subset (`id 1-200`), which is translated into 101 languages. Furthermore, the field `id` can be used to match the translations of the same data instance across languages. - The `source_id` field contains the corresponding original row index from the [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset.
dolly-machine-translated ```json { "id": 2, "inputs": "How to escape from a helicopter trapped in water ?", "targets": "If you are ever trapped inside a helicopter while submerged in water, it’s best to try and remain calm until the cabin is completely underwater. It’s better to wait for pressure to be equalized, before you try to open the door or break the glass to escape.", "language": "eng", "script": "Latn", "source_id": 6060, } ```
dolly-human-edited ```json { "id": 2, "inputs": "Comment peut-on s'échapper d'un hélicoptère piégé dans l'eau ?", "targets": "-", "language": "fra", "script": "Latn", "source_id": 6060, } ```
## Statistics The toggled table below lists the breakdown of languages in each subset. ### Languages
aya-human-annotated | ISO Code | Language | Resources | |----------|----------|---------------| | `tel` | Telugu | Low | | `yor` | Yorùbá | Low | | `arb` | Arabic | High | | `tur` | Turkish | High | | `por` | Portuguese | High | | `zho` | Chinese (Simplified) | High | | `eng` | English | High |
dolly-machine-translated | ISO Code | Language | Resources | |----------|----------|-----------| | `ace` | Achinese | Low | | `afr` | Afrikaans | Mid | | `amh` | Amharic | Low | | `ara` (`arb`, `acm`, `acq`, `aeb`, `ajp`, `apc`, `ars`, `ary` & `arz`) | Arabic (Standard, Gelet Iraqi, Ta'izzi-Adeni, Tunisian, South Levantine, North Levantine, Najdi, Moroccan & Egyptian) | High | | `aze` (`azb` & `azj`) | Azerbaijani (South & North) | Low | | `bel` | Belarusian | Mid | | `ben` | Bengali | Mid | | `bjn` | Banjar | Low | | `bul` | Bulgarian | Mid | | `cat` | Catalan | High | | `ceb` | Cebuano | Mid | | `ces` | Czech | High | | `cym` | Welsh | Low | | `dan` | Danish | Mid | | `deu` | German | High | | `ell` | Greek | Mid | | `eng` | English | High | | `epo` | Esperanto | Low | | `est` | Estonian | Mid | | `eus` | Basque | High | | `fin` | Finnish | High | | `fra` | French | High | | `gla` | Scottish Gaelic | Low | | `gle` | Irish | Low | | `glg` | Galician | Mid | | `guj` | Gujarati | Low | | `hat` | Haitian Creole | Low | | `hau` | Hausa | Low | | `heb` | Hebrew | Mid | | `hin` | Hindi | High | | `hun` | Hungarian | High | | `hye` | Armenian | Low | | `ibo` | Igbo | Low | | `ind` | Indonesian | Mid | | `isl` | Icelandic | Low | | `ita` | Italian | High | | `jav` | Javanese | Low | | `jpn` | Japanese | High | | `kan` | Kannada | Low | | `kas` | Kashmiri | Low | | `kat` | Georgian | Mid | | `kau` (`knc`) | Kanuri (Central) | Low | | `kaz` | Kazakh | Mid | | `khm` | Khmer | Low | | `kir` | Kyrgyz | Low | | `kor` | Korean | High | | `kur` (`ckb` & `kmr`) | Kurdish (Central & Northern) | Low | | `lao` | Lao | Low | | `lav` (`lvs`) | Latvian (Standard) | Mid | | `lit` | Lithuanian | Mid | | `ltz` | Luxembourgish | Low | | `mal` | Malayalam | Low | | `mar` | Marathi | Low | | `min` | Minangkabau | Low | | `mkd` | Macedonian | Low | | `mlg` (`plt`) | Malagasy (Plateau) | Low | | `mlt` | Maltese | Low | | `mni` | Manipuri | Low | | `mon` (`khk`) | Mongolian (Khalkha) | Low | | `mri` | Maori | Low | | `msa` (`zsm`) | Malay (Standard) | Mid | | `mya` | Burmese | Low | | `nep` (`npi`) | Nepali | Low | | `nld` | Dutch | High | | `nor` (`nno` & `nob`) | Norwegian (Nynorsk & Bokmål) | Low | | `nso` | Northern Sotho | Low | | `pes` | Persian | High | | `pol` | Polish | High | | `por` | Portuguese | High | | `pus` (`pbt`) | Pashto (Southern) | Low | | `ron` | Romanian | Mid | | `rus` | Russian | High | | `sin` | Sinhala | Low | | `slk` | Slovak | Mid | | `slv` | Slovenian | Mid | | `smo` | Samoan | Low | | `sna` | Shona | Low | | `snd` | Sindhi | Low | | `som` | Somali | Low | | `sot` | Southern Sotho | Low | | `spa` | Spanish | High | | `sqi` (`als`) | Albanian (Tosk) | Low | | `srp` | Serbian | High | | `sun` | Sundanese | Low | | `swa` (`swh`) | Swahili (Coastal) | Low | | `swe` | Swedish | High | | `tam` | Tamil | Mid | | `taq` | Tamasheq | Low | | `tel` | Telugu | Low | | `tgk` | Tajik | Low | | `tha` | Thai | Mid | | `tur` | Turkish | High | | `ukr` | Ukrainian | Mid | | `urd` | Urdu | Mid | | `uzb` (`uzn`) | Uzbek (Nothern) | Mid | | `vie` | Vietnamese | High | | `xho` | Xhosa | Low | | `yid` (`ydd`) | Yiddish (Eastern) | Low | | `yor` | Yoruba | Low | | `zho` (+ `yue`) | Chinese (Simplified & Cantonese) | High | | `zul` | Zulu | Low |
dolly-human-edited | ISO Code | Language | Resources | |----------|----------|-----------| | `arb` | Arabic | High | | `fra` | French | High | | `hin` | Hindi | High | | `rus` | Russian | High | | `spa` | Spanish | High | | `srp` | Serbian | High |

# Motivations & Intentions - **Curation Rationale:** This evaluation suite is tailored to test the generation quality of multilingual models, with the aim of balancing language coverage and human-sourced quality. It covers prompts originally written in each language, as well as English-centric translated, and manually curated or edited prompts for a linguistically broad, but rich testbed. The list of languages was initially established from mT5 and aligned with the annotators’ language list and the NLLB translation model. # Known Limitations - **Translation Quality:** Note that the expressiveness of the `dolly-machine-translated` subset is limited by the quality of the translation model and may adversely impact an estimate of ability in languages where translations are not adequate. If this subset is used for testing, we recommend it be paired and reported with the professionally post-edited `dolly-human-edited` subset or the `aya-human-annotated` set, which, while covering only 7 languages, is entirely created by proficient target language speakers. --- # Additional Information ## Provenance - **Methods Used:** combination of original annotations by volunteers, automatic translation, and post-editing of translations by professional annotators. - **Methodology Details:** - *Source:* Original annotations from Aya dataset along with translations and post-edits of Dolly dataset - *Platform:* [Aya Annotation Platform](https://aya.for.ai/) - *Dates of Collection:* May 2023 - Dec 2023 ## Dataset Version and Maintenance - **Maintenance Status:** Actively Maintained - **Version Details:** - *Current version:* 1.0 - *Last Update:* 02/2024 - *First Release:* 02/2024 - **Maintenance Plan:** No updates planned. ## Authorship - **Publishing Organization:** [Cohere For AI](https://cohere.com/research) - **Industry Type:** Not-for-profit - Tech - **Contact Details:** https://aya.for.ai/ ## Licensing Information This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License. ## Citation Information ```bibtex @misc{singh2024aya, title={Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning}, author={Shivalika Singh and Freddie Vargus and Daniel Dsouza and Börje F. Karlsson and Abinaya Mahendiran and Wei-Yin Ko and Herumb Shandilya and Jay Patel and Deividas Mataciunas and Laura OMahony and Mike Zhang and Ramith Hettiarachchi and Joseph Wilson and Marina Machado and Luisa Souza Moura and Dominik Krzemiński and Hakimeh Fadaei and Irem Ergün and Ifeoma Okoh and Aisha Alaagib and Oshan Mudannayake and Zaid Alyafeai and Vu Minh Chien and Sebastian Ruder and Surya Guthikonda and Emad A. Alghamdi and Sebastian Gehrmann and Niklas Muennighoff and Max Bartolo and Julia Kreutzer and Ahmet Üstün and Marzieh Fadaee and Sara Hooker}, year={2024}, eprint={2402.06619}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```