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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. 😉


Of the 500+ languages listed at, 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:

This would produce an output:

{'image_id': ['4e9bdde5-996d-4a98-ac1c-d80fb6349314',
 'image_url': ['',
 'story_index': [0, 1, 2, 3, 4, 5, 6],
 'story_id': ['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.


  • 6 December 2022 - dataset is made public
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