Datasets:

sil-ai
/

bloom-vist

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - afr
  - af
  - aaa
  - abc
  - ada
  - adq
  - aeu
  - agq
  - ags
  - ahk
  - aia
  - ajz
  - aka
  - ak
  - ame
  - amh
  - am
  - amp
  - amu
  - ann
  - aph
  - awa
  - awb
  - azn
  - azo
  - bag
  - bam
  - bm
  - baw
  - bax
  - bbk
  - bcc
  - bce
  - bec
  - bef
  - ben
  - bn
  - bfd
  - bfm
  - bfn
  - bgf
  - bho
  - bhs
  - bis
  - bi
  - bjn
  - bjr
  - bkc
  - bkh
  - bkm
  - bkx
  - bob
  - bod
  - bo
  - boz
  - bqm
  - bra
  - brb
  - bri
  - brv
  - bss
  - bud
  - buo
  - bwt
  - bwx
  - bxa
  - bya
  - bze
  - bzi
  - cak
  - cbr
  - ceb
  - cgc
  - chd
  - chp
  - cim
  - clo
  - cmn
  - zh
  - cmo
  - csw
  - cuh
  - cuv
  - dag
  - ddg
  - ded
  - deu
  - de
  - dig
  - dje
  - dmg
  - dnw
  - dtp
  - dtr
  - dty
  - dug
  - eee
  - ekm
  - enb
  - enc
  - eng
  - en
  - ewo
  - fas
  - fa
  - fil
  - fli
  - fon
  - fra
  - fr
  - fub
  - fuh
  - gal
  - gbj
  - gou
  - gsw
  - guc
  - guj
  - gu
  - guz
  - gwc
  - hao
  - hat
  - ht
  - hau
  - ha
  - hbb
  - hig
  - hil
  - hin
  - hi
  - hla
  - hna
  - hre
  - hro
  - idt
  - ilo
  - ind
  - id
  - ino
  - isu
  - ita
  - it
  - jgo
  - jmx
  - jpn
  - ja
  - jra
  - kak
  - kam
  - kan
  - kn
  - kau
  - kr
  - kbq
  - kbx
  - kby
  - kek
  - ken
  - khb
  - khm
  - km
  - kik
  - ki
  - kin
  - rw
  - kir
  - ky
  - kjb
  - kmg
  - kmr
  - ku
  - kms
  - kmu
  - kor
  - ko
  - kqr
  - krr
  - ksw
  - kur
  - ku
  - kvt
  - kwd
  - kwu
  - kwx
  - kxp
  - kyq
  - laj
  - lan
  - lao
  - lo
  - lbr
  - lfa
  - lgg
  - lgr
  - lhm
  - lhu
  - lkb
  - llg
  - lmp
  - lns
  - loh
  - lsi
  - lts
  - lug
  - lg
  - luy
  - lwl
  - mai
  - mal
  - ml
  - mam
  - mar
  - mr
  - mdr
  - mfh
  - mfj
  - mgg
  - mgm
  - mgo
  - mgq
  - mhx
  - miy
  - mkz
  - mle
  - mlk
  - mlw
  - mmu
  - mne
  - mnf
  - mnw
  - mot
  - mqj
  - mrn
  - mry
  - msb
  - muv
  - mve
  - mxu
  - mya
  - my
  - myk
  - myx
  - mzm
  - nas
  - nco
  - nep
  - ne
  - new
  - nge
  - ngn
  - nhx
  - njy
  - nla
  - nld
  - nl
  - nlv
  - nod
  - nsk
  - nsn
  - nso
  - nst
  - nuj
  - nwe
  - nwi
  - nxa
  - nxl
  - nya
  - ny
  - nyo
  - nyu
  - nza
  - odk
  - oji
  - oj
  - oki
  - omw
  - ori
  - or
  - ozm
  - pae
  - pag
  - pan
  - pa
  - pbt
  - pce
  - pcg
  - pdu
  - pea
  - pex
  - pis
  - pkb
  - pmf
  - pnz
  - por
  - pt
  - psp
  - pwg
  - qaa
  - qub
  - quc
  - quf
  - quz
  - qve
  - qvh
  - qvm
  - qvo
  - qxh
  - rel
  - rnl
  - ron
  - ro
  - roo
  - rue
  - rug
  - rus
  - ru
  - san
  - sa
  - saq
  - sat
  - sdk
  - sea
  - sgd
  - shn
  - sml
  - snk
  - snl
  - som
  - so
  - sot
  - st
  - sox
  - spa
  - es
  - sps
  - ssn
  - stk
  - swa
  - sw
  - swh
  - sxb
  - syw
  - taj
  - tam
  - ta
  - tbj
  - tdb
  - tdg
  - tdt
  - teo
  - tet
  - tgk
  - tg
  - tha
  - th
  - the
  - thk
  - thl
  - thy
  - tio
  - tkd
  - tnl
  - tnn
  - tnp
  - tnt
  - tod
  - tom
  - tpi
  - tpl
  - tpu
  - tsb
  - tsn
  - tn
  - tso
  - ts
  - tuv
  - tuz
  - tvs
  - udg
  - unr
  - urd
  - ur
  - uzb
  - uz
  - ven
  - ve
  - vie
  - vi
  - vif
  - war
  - wbm
  - wbr
  - wms
  - wni
  - wnk
  - wtk
  - xho
  - xh
  - xkg
  - xmd
  - xmg
  - xmm
  - xog
  - xty
  - yas
  - yav
  - ybb
  - ybh
  - ybi
  - ydd
  - yea
  - yet
  - yid
  - yi
  - yin
  - ymp
  - zaw
  - zho
  - zh
  - zlm
  - zuh
  - zul
  - zu
license:
  - cc-by-4.0
  - cc-by-nc-4.0
  - cc-by-nd-4.0
  - cc-by-sa-4.0
  - cc-by-nc-nd-4.0
  - cc-by-nc-sa-4.0
multilinguality:
  - multilingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
paperswithcode_id: null
pretty_name: BloomVIST
extra_gated_prompt: >-
  One more step before getting this dataset. This dataset is open access and
  available only for non-commercial use (except for portions of the dataset
  labeled with a `cc-by-sa` license). A "license" field paired with each of the
  dataset entries/samples specifies the Creative Commons license for that
  entry/sample.


  These [Creative Commons
  licenses](https://creativecommons.org/about/cclicenses/) specify that: 


  1. You cannot use the dataset for or directed toward commercial advantage or
  monetary compensation (except for those portions of the dataset labeled
  specifically with a `cc-by-sa` license. If you would like to ask about
  commercial uses of this dataset, please [email us](mailto:sj@derivation.co).

  2. Any public, non-commercial use of the data must give appropriate credit,
  provide a link to the license, and indicate if changes were made. You may do
  so in any reasonable manner, but not in any way that suggests the licensor
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  remix, transform, or build upon the material, and you may not distribute
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  In addition to the above implied by Creative Commons and when clicking "Access
  Repository" below, you agree: 


  1. Not to use the dataset for any use intended to or which has the effect of
  harming or enabling discrimination against individuals or groups based on
  legally protected characteristics or categories, including but not limited to
  discrimination against Indigenous People as outlined in Articles 2; 13-16; and
  31 of the United Nations Declaration on the Rights of Indigenous People, 13
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  2. That your *contact information* (email address and username) can be shared
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Dataset Description
- Dataset Summary
- Languages

Dataset Structure

Data Instances
Data Fields

Data Splits Dataset Description

Homepage: SIL AI
Point of Contact: SIL AI email
Source Data: Bloom Library

Dataset Summary

Bloom is free, open-source software and an associated website Bloom Library, app, and services developed by SIL International. Bloom’s primary goal is to equip non-dominant language communities and their members to create the literature they want for their community and children. Bloom also serves organizations that help such communities develop literature and education or other aspects of community development.

This version of the Bloom Library data is developed specifically for the visual story telling (or VIST) task. It includes data from 364 languages across 31 language families. There is a mean of 32 stories and median of 2 stories per language.

Note: If you speak one of these languages and can help provide feedback or corrections, please let us know!

Note: Although this data was used in the training of the BLOOM model, this dataset only represents a small portion of the data used to train that model. Data from "Bloom Library" was combined with a large number of other datasets to train that model. "Bloom Library" is a project that existed prior to the BLOOM model, and is something separate. All that to say... We were using the "Bloom" name before it was cool. 😉

Languages

Of the 500+ languages listed at BloomLibrary.org, there are 363 languages available in this dataset. Here are the corresponding ISO 639-3 codes:

aaa, abc, ada, adq, aeu, afr, agq, ags, ahk, aia, ajz, aka, ame, amh, amp, amu, ann, aph, awa, awb, azn, azo, bag, bam, baw, bax, bbk, bcc, bce, bec, bef, ben, bfd, bfm, bfn, bgf, bho, bhs, bis, bjn, bjr, bkc, bkh, bkm, bkx, bob, bod, boz, bqm, bra, brb, bri, brv, bss, bud, buo, bwt, bwx, bxa, bya, bze, bzi, cak, cbr, ceb, cgc, chd, chp, cim, clo, cmn, cmo, csw, cuh, cuv, dag, ddg, ded, deu, dig, dje, dmg, dnw, dtp, dtr, dty, dug, eee, ekm, enb, enc, eng, ewo, fas, fil, fli, fon, fra, fub, fuh, gal, gbj, gou, gsw, guc, guj, guz, gwc, hao, hat, hau, hbb, hig, hil, hin, hla, hna, hre, hro, idt, ilo, ind, ino, isu, ita, jgo, jmx, jpn, jra, kak, kam, kan, kau, kbq, kbx, kby, kek, ken, khb, khm, kik, kin, kir, kjb, kmg, kmr, kms, kmu, kor, kqr, krr, ksw, kur, kvt, kwd, kwu, kwx, kxp, kyq, laj, lan, lao, lbr, lfa, lgg, lgr, lhm, lhu, lkb, llg, lmp, lns, loh, lsi, lts, lug, luy, lwl, mai, mal, mam, mar, mdr, mfh, mfj, mgg, mgm, mgo, mgq, mhx, miy, mkz, mle, mlk, mlw, mmu, mne, mnf, mnw, mot, mqj, mrn, mry, msb, muv, mve, mxu, mya, myk, myx, mzm, nas, nco, nep, new, nge, ngn, nhx, njy, nla, nld, nlv, nod, nsk, nsn, nso, nst, nuj, nwe, nwi, nxa, nxl, nya, nyo, nyu, nza, odk, oji, oki, omw, ori, ozm, pae, pag, pan, pbt, pce, pcg, pdu, pea, pex, pis, pkb, pmf, pnz, por, psp, pwg, qub, quc, quf, quz, qve, qvh, qvm, qvo, qxh, rel, rnl, ron, roo, rue, rug, rus, san, saq, sat, sdk, sea, sgd, shn, sml, snk, snl, som, sot, sox, spa, sps, ssn, stk, swa, swh, sxb, syw, taj, tam, tbj, tdb, tdg, tdt, teo, tet, tgk, tha, the, thk, thl, thy, tio, tkd, tnl, tnn, tnp, tnt, tod, tom, tpi, tpl, tpu, tsb, tsn, tso, tuv, tuz, tvs, udg, unr, urd, uzb, ven, vie, vif, war, wbm, wbr, wms, wni, wnk, wtk, xho, xkg, xmd, xmg, xmm, xog, xty, yas, yav, ybb, ybh, ybi, ydd, yea, yet, yid, yin, ymp, zaw, zho, zlm, zuh, zul

Dataset Statistics

Some of the languages included in the dataset just include 1 or a couple of "stories." For those with higher numbers of available stories we include the following numbers of stories:

ISO639-3 Code	Stories	Image-Caption Pairs
ahk	55	493
awa	163	1200
ben	220	1938
bho	172	1163
bis	21	183
brb	22	330
bzi	66	497
cak	50	694
ceb	394	2806
cgc	182	1473
deu	22	250
dty	172	1310
eng	2187	24338
fas	128	620
fil	34	366
fra	315	4350
hat	224	1881
hau	229	1594
ind	232	1866
jra	56	575
kak	195	1416
kek	21	419
khb	31	167
khm	26	246
kir	278	2866
kjb	63	584
kor	129	2732
krr	29	362
lsi	22	173
mai	177	1186
mam	118	1058
mhx	51	544
myk	22	214
nep	194	1464
new	177	1225
pbt	203	979
por	148	2939
quc	99	817
rus	271	2977
snk	21	210
spa	444	5201
swh	34	387
tdg	31	231
tha	275	2929
thl	185	1464
tpi	137	1528
tpu	28	513
zho	42	339

Dataset Structure

Data Instances

The examples look like this for Hindi:

from datasets import load_dataset

# Specify the language code.
dataset = load_dataset("sil-ai/bloom-vist", 'hin')

# An individual samples consists of stories in the specified language code.
# To see a story:
print(dataset['train'][0]['story'])

This would produce an output:

{'image_id': ['4e9bdde5-996d-4a98-ac1c-d80fb6349314',
  '614e4d51-bbdb-4538-98d3-f603c12dccd0',
  '970d60bf-2acb-44ac-8ffb-5aa3f7989630',
  'd4ad1199-863e-4929-a377-93276fe5caa8',
  '0d9ad694-995a-433d-af4e-6f40ddfa208a',
  '811176eb-c9f3-4226-8af5-e6c4e524c494',
  '83180da7-4ba8-4104-a0d9-49aa2ef48f7a'],
 'image_url': ['https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_03_Image_00011.png',
  'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_04_Image_0001.png',
  'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_05_Image_0001.png',
  'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_06_Image_0001.png',
  'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_0001.png',
  'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_00011.png',
  'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_09_Image_0001.png'],
 'story_index': [0, 1, 2, 3, 4, 5, 6],
 'story_id': ['cc34c1c7-c086-491b-8e6a-65572e1efdb6',
  'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
  'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
  'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
  'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
  'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
  'cc34c1c7-c086-491b-8e6a-65572e1efdb6'],
 'text': ['साबू ने एक कंकड़ को ठोकर मारी। कंकड़ लुढ़कता हुआ एक पेड़ के पास पहुँचा। पेड़ के तने पर मुलायम बाल थे। साबू ने छुए और ऊपर देखा, ऊपर, ऊपर और उससे भी ऊपर...दो आँखें नीचे देख रही थीं।',
  '“हेलो, तुम कौन हो?” साबू को बड़ा अचम्भा हुआ।“हेलो, मैं जिराफ़ हूँ। मेरा नाम है जोजो। \xa0मैं तुम्हारे साथ खेल सकता हूँ। मेरी पीठ पर चढ़ जाओ, मैं तुम्हें घुमा के लाता हूँ।”',
  'साबू जोजो की पीठ पर चढ़ गया और वे सड़क पर चल निकले। फिर पहाड़ी पर और शहर के बीचों बीच।\nसाबू खुशी से चिल्लाया, “जोजो दाएँ मुड़ो,\n                                बाएँ मुड़ो और फिर दाएँ।” अब वे उसकी दोस्त मुन्नी के घर पहुँच गये।',
  'आज मुन्नी का जन्मदिन था। साबू को जोजो पर सवारी करते देख बच्चों ने ताली बजायी।\xa0\n                                जोजो ने गुब्बारे लटकाने में आन्टी की मदद करी क्योंकि वह इतना... लम्बा था।\xa0\n                                कितना आसान था!',
  'जोजो ने सब बच्चों को सवारी कराई।\n                                उनके साथ बॉल भी खेली। बड़े मज़े की पार्टी थी।सब ने गाया, “हैप्पी बर्थ डे टु यू ।”\n                                        आन्टी ने मेज़ पर समोसे, गुलाब जामुन और आइसक्रीम सजाई।',
  'जोजो को आइसक्रीम बहुत पसन्द आई। अंकल उसके लिये एक बाल्टी भर के आइसक्रीम लाये। जोजो ने पूरी बाल्टी ख़त्म कर दी। \xa0अब घर जाने का समय हो गया।\n\nसब ने कहा, “बाय बाय जोजो, बाय बाय साबू।” साबू और जोजो घर लौटे।',
  '']}

Data Fields

The metadata fields below are available. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).

id: id of the sample
title: title of the book, e.g. "Going to Buy a Book".
license: specific license used, e.g. "cc-by-sa" for "Creative Commons, by attribution, share-alike".
album_id: an ID value corresponding to the set of images corresponding to the given story
story: the sequenced story data including lists of image IDs, image URLs, and corresponding text

Data Splits

Currently all languages include a train split only. In the future, we will be creating manual splits of the data.

Changelog

6 December 2022 - dataset is made public

Table of Contents

Dataset Summary

Languages

Dataset Statistics

Dataset Structure

Data Instances

Data Fields

Data Splits

Changelog