goldfish-models/tpi_latn_full
Text Generation
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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 language modeling 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." These are not split between training, validation, and test. For those with higher numbers of available stories we include the following numbers of stories in each split:
| ISO 639-3 | Name | Train Stories | Validation Stories | Test Stories |
|---|---|---|---|---|
| aeu | Akeu | 47 | 6 | 5 |
| afr | Afrikaans | 19 | 2 | 2 |
| ahk | Akha | 81 | 10 | 10 |
| aph | Athpariya | 28 | 4 | 3 |
| awa | Awadhi | 131 | 16 | 16 |
| ben | Bengali | 201 | 25 | 25 |
| bfn | Bunak | 11 | 1 | 1 |
| bho | Bhojpuri | 139 | 17 | 17 |
| bis | Bislama | 20 | 2 | 2 |
| bkm | Kom (Cameroon) | 15 | 2 | 1 |
| bkx | Baikeno | 8 | 1 | 1 |
| brb | Brao | 18 | 2 | 2 |
| bwx | Bu-Nao Bunu | 14 | 2 | 1 |
| bzi | Bisu | 53 | 7 | 6 |
| cak | Kaqchikel | 54 | 7 | 6 |
| cbr | Cashibo-Cacataibo | 11 | 1 | 1 |
| ceb | Cebuano | 335 | 42 | 41 |
| cgc | Kagayanen | 158 | 20 | 19 |
| cmo | Central Mnong | 16 | 2 | 2 |
| ddg | Fataluku | 14 | 2 | 1 |
| deu | German | 36 | 4 | 4 |
| dtp | Kadazan Dusun | 13 | 2 | 1 |
| dty | Dotyali | 138 | 17 | 17 |
| eng | English | 2107 | 263 | 263 |
| fas | Persian | 104 | 13 | 12 |
| fil | Filipino | 55 | 7 | 6 |
| fra | French | 323 | 40 | 40 |
| gal | Galolen | 11 | 1 | 1 |
| gwc | Gawri | 15 | 2 | 1 |
| hat | Haitian | 208 | 26 | 26 |
| hau | Hausa | 205 | 26 | 25 |
| hbb | Huba | 22 | 3 | 2 |
| hin | Hindi | 16 | 2 | 2 |
| idt | Idaté | 8 | 1 | 1 |
| ind | Indonesian | 208 | 26 | 25 |
| jmx | Western Juxtlahuaca Mixtec | 19 | 2 | 2 |
| jra | Jarai | 112 | 14 | 13 |
| kak | Kalanguya | 156 | 20 | 19 |
| kan | Kannada | 17 | 2 | 2 |
| kau | Kanuri | 36 | 5 | 4 |
| kek | Kekchí | 29 | 4 | 3 |
| khb | Lü | 25 | 3 | 3 |
| khm | Khmer | 28 | 4 | 3 |
| kik | Kikuyu | 8 | 1 | 1 |
| kir | Kirghiz | 306 | 38 | 38 |
| kjb | Q'anjob'al | 82 | 10 | 10 |
| kmg | Kâte | 16 | 2 | 1 |
| kor | Korean | 106 | 13 | 13 |
| krr | Krung | 24 | 3 | 3 |
| kwd | Kwaio | 19 | 2 | 2 |
| kwu | Kwakum | 16 | 2 | 2 |
| lbr | Lohorung | 8 | 1 | 1 |
| lhu | Lahu | 32 | 4 | 4 |
| lsi | Lashi | 21 | 3 | 2 |
| mai | Maithili | 144 | 18 | 18 |
| mal | Malayalam | 12 | 1 | 1 |
| mam | Mam | 108 | 13 | 13 |
| mar | Marathi | 8 | 1 | 1 |
| mgm | Mambae | 12 | 2 | 1 |
| mhx | Maru | 79 | 10 | 9 |
| mkz | Makasae | 16 | 2 | 2 |
| mya | Burmese | 31 | 4 | 3 |
| myk | Mamara Senoufo | 28 | 3 | 3 |
| nep | Nepali (macrolanguage) | 160 | 20 | 20 |
| new | Newari | 142 | 18 | 17 |
| nlv | Orizaba Nahuatl | 8 | 1 | 1 |
| nsn | Nehan | 9 | 1 | 1 |
| nwi | Southwest Tanna | 9 | 1 | 1 |
| nxa | Nauete | 12 | 1 | 1 |
| omw | South Tairora | 10 | 1 | 1 |
| pbt | Southern Pashto | 164 | 21 | 20 |
| pce | Ruching Palaung | 30 | 4 | 3 |
| pis | Pijin | 14 | 2 | 1 |
| por | Portuguese | 131 | 16 | 16 |
| quc | K'iche' | 80 | 10 | 9 |
| rus | Russian | 283 | 35 | 35 |
| sdk | Sos Kundi | 9 | 1 | 1 |
| snk | Soninke | 28 | 4 | 3 |
| spa | Spanish | 423 | 53 | 52 |
| swh | Swahili (individual language) | 58 | 7 | 7 |
| tam | Tamil | 13 | 2 | 1 |
| tdg | Western Tamang | 26 | 3 | 3 |
| tdt | Tetun Dili | 22 | 3 | 2 |
| tet | Tetum | 8 | 1 | 1 |
| tgk | Tajik | 24 | 3 | 2 |
| tha | Thai | 228 | 29 | 28 |
| the | Chitwania Tharu | 11 | 1 | 1 |
| thl | Dangaura Tharu | 148 | 19 | 18 |
| tnl | Lenakel | 10 | 1 | 1 |
| tnn | North Tanna | 9 | 1 | 1 |
| tpi | Tok Pisin | 161 | 20 | 20 |
| tpu | Tampuan | 24 | 3 | 2 |
| uzb | Uzbek | 24 | 3 | 2 |
| war | Waray (Philippines) | 16 | 2 | 2 |
| wbr | Wagdi | 10 | 1 | 1 |
| wni | Ndzwani Comorian | 12 | 2 | 1 |
| xkg | Kagoro | 16 | 2 | 1 |
| ybh | Yakha | 16 | 2 | 1 |
| zho | Chinese | 34 | 4 | 4 |
| zlm | Malay (individual language) | 8 | 1 | 1 |
| zul | Zulu | 19 | 2 | 2 |
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-lm", 'hin')
# A data point consists of stories in the specified language code.
# To see a story:
print(dataset['train']['text'][0])
This would produce an output:
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। मैं 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।
साबू खुशी से चिल्लाया, “जोजो दाएँ मुड़ो,
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।
जोजो ने गुब्बारे लटकाने में आन्टी की मदद करी क्योंकि वह इतना... लम्बा था।
कितना आसान था!
जोजो ने सब बच्चों को सवारी कराई।
उनके साथ बॉल भी खेली। बड़े मज़े की पार्टी थी।सब ने गाया, “हैप्पी बर्थ डे टु यू ।”
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। अब घर जाने का समय हो गया।
सब ने कहा, “बाय बाय जोजो, बाय बाय साबू।” साबू और जोजो घर लौटे।
Whereas if you wish to gather all the text for a language you may use this:
dataset['train']['text']
Data Fields
The metadata fields below are available and the full dataset will be updated with per story metadata soon (in August 2022). As of now a majority of stories have metadata, but some are missing certain fields. 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).
- text: the text of the story/book, concatenated together from the different pages.
- 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".
- copyright: copyright notice from the original book on bloomlibrary.org
- pageCount: page count from the metadata on the original book on bloomlibrary.org.
- bookInstanceId: unique ID for each book/translation assigned by Bloom. For example the Hindi version of 'Going to Buy a Book' is 'af86eefd-f69c-4e06-b8eb-e0451853aab9'.
- bookLineage: Unique bookInstanceIDs of other Bloom books that this book is in some way based on. For example, the Hindi version in the example above is based on '056B6F11-4A6C-4942-B2BC-8861E62B03B3'. It's quite possible for this to be either empty, or have multiple entries. For example, the book 'Saboo y Jojo' with ID '5b232a5f-561d-4514-afe7-d6ed2f6a940f' is based on two others, ['056B6F11-4A6C-4942-B2BC-8861E62B03B3', '10a6075b-3c4f-40e4-94f3-593497f2793a']
- (coming soon) contentLanguages: Other languages this book may be available in. "Going to Buy a Book" is available in ['eng', 'kan', 'mar', 'pan', 'ben', 'guj', 'hin'] for example.
Data Splits
All languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.
Changelog
- 25 August 2022 - add the remaining metadata, change data type of
pageCountto int32 - 24 August 2022 - majority of metadata added back in to the filtered/ clean data
- 23 August 2022 - metadata temporarily removed to update to cleaner dataset
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