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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. 😉
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
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