metadata

license: apache-2.0
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
    splits:
      - name: test
        num_bytes: 1209511
        num_examples: 1200
    download_size: 599142
    dataset_size: 1209511
  - 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: 39488955
        num_examples: 23800
    download_size: 20063815
    dataset_size: 39488955
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-*
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
language_creators:
  - crowdsourced
  - expert-generated
  - machine-generated
multilinguality:
  - multilingual
pretty_name: Aya Evaluation Suite
size_categories:
  - 10K<n<100K
source_datasets:
  - original
  - extended
task_categories:
  - text-generation

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:

human-curated examples in 7 languages (tur,eng,yor,arb,zho,por,tel).
machine-translations of handpicked examples into 101 languages.
human-post-edited translations into 6 languages (hin,srp,rus,fra,arb,spa).

Curated by: Contributors of Aya Open Science Intiative, professional annotators, and synthetic generation
Language(s): 101 languages
License: Apache 2.0

Aya Datasets Family:

Name	Explanation
aya_dataset	Human-annotated multilingual instruction finetuning dataset, comprising over 204K instances across 65 languages.
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 513k instances for various tasks.
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:

aya-human-annotated: 250 original human-written prompts in 7 languages each.
dolly-machine-translated: 200 human-selected prompts from databricks-dolly-15k , automatically translated with the NLLB model from English into 101 languages (114 dialects in total).
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:

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:

{
"id": 42,
"prompt": "What day is known as Star Wars Day?",
"completion": "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

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 & Yue)	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 established from mT5 and aligned with the annotators’ language list and the NLLB translation model.

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
- 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
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 License.

Citation Information

@misc{ayadata2024,
    title = {Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning},
    author = {Shivalika Singh, Freddie Vargus, Daniel D'souza, Börje F. Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura O'Mahony, Mike Zhang, Ramith Hettiarachchi, Joseph Wilson, Marina Machado, Luisa Souza Moura, Dominik Krzemiński, Hakimeh Fadaei, Irem Ergün, Ifeoma Okoh, Aisha Alaagib, Oshan Mudannayake, Zaid Alyafeai, Vu Minh Chien, Sebastian Ruder, Surya Guthikonda, Emad A. Alghamdi, Sebastian Gehrmann, Niklas Muennighoff, Max Bartolo, Julia Kreutzer, Ahmet Üstün, Marzieh Fadaee, Sara Hooker},
    year = 2024,
}