---
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- afr
- sqi
- amh
- ara
- aze
- bel
- ben
- bul
- cat
- ceb
- ces
- kur
- cym
- dan
- deu
- ell
- eng
- epo
- est
- eus
- fin
- fra
- gla
- gle
- glg
- guj
- hat
- hau
- heb
- hin
- hun
- hye
- ibo
- ind
- isl
- ita
- jav
- jpn
- kan
- kat
- kaz
- mon
- khm
- kir
- kor
- lao
- lit
- ltz
- lav
- mal
- mar
- mkd
- mlt
- mri
- mya
- nld
- nor
- nep
- sot
- pus
- pes
- mlg
- pol
- por
- ron
- rus
- sin
- slk
- slv
- smo
- sna
- snd
- som
- spa
- srp
- sun
- swe
- swa
- tam
- tel
- tgk
- tha
- tur
- ukr
- urd
- uzb
- vie
- xho
- yid
- yor
- zho
- msa
- zul
- ace
- bjn
- kas
- kau
- min
- mni
- taq
- nso
license: apache-2.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
- extended
task_categories:
- text-generation
pretty_name: Aya Evaluation Suite
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: 1624958
    num_examples: 1750
  download_size: 974483
  dataset_size: 1624958
- config_name: dolly_human_edited
  features:
  - name: id
    dtype: int64
  - name: inputs
    dtype: string
  - name: targets
    dtype: string
  - name: language
    dtype: string
  - name: script
    dtype: string
  - name: source_id
    dtype: int64
  splits:
  - name: test
    num_bytes: 1219111
    num_examples: 1200
  download_size: 602117
  dataset_size: 1219111
- 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
  - name: source_id
    dtype: int64
  splits:
  - name: test
    num_bytes: 39679355
    num_examples: 23800
  download_size: 20100505
  dataset_size: 39679355
configs:
- config_name: aya_human_annotated
  data_files:
  - split: test
    path: aya_human_annotated/test-*
- config_name: dolly_human_edited
  data_files:
  - split: test
    path: dolly_human_edited/test-*
- config_name: dolly_machine_translated
  data_files:
  - split: test
    path: dolly_machine_translated/test-*
---

![Aya Header](https://huggingface.co/datasets/CohereForAI/aya_dataset/resolve/main/aya_header.png)

# Dataset Summary

`Aya Evaluation Suite` contains a total of 25,750 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`).
2) machine-translations of handpicked examples into 101 languages.
3) human-post-edited translations into 6 languages (`hin,srp,rus,fra,arb,spa`).

---

- **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 both prompt-completions and demographics data with `datasets`, you'll 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.

## Data Instances

An example of the `Aya Evaluation Suite` looks as follows:

```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",
}
```

## Statistics

The toggled table below lists the breakdown of languages in each subset.

### Languages

<details>
<summary> <b>aya-human-annotated</b> </summary>

  | 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 |
  
</details>


<details>
<summary> <b>dolly-machine-translated</b> </summary>
  
  | 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 & Yue) |	High	|
  |	`zul`  |	Zulu	|	Low	|
</details>

<details>
<summary> <b>dolly-human-edited</b> </summary>
  
  | ISO Code | Language | Resources |
  |----------|----------|-----------|
  | `arb` | Arabic | High |
  | `fra` | French | High |
  | `hin` | Hindi | High |
  | `rus` | Russian | High |
  | `spa` | Spanish | High |
  | `srp` | Serbian | High |

</details>

<br>

# 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 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 also only covers 7 languages but 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 and translations and post-edits of Dolly
    - *Platform:* [Aya Annotation Platform](https://aya.for.ai/)
    - *Dates of Collection:* Jun 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}
}
```