bloom-2b5 / README.md

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	---
	license: bigscience-bloom-rail-1.0
	language:
	- ak
	- ar
	- as
	- bm
	- bn
	- ca
	- code
	- en
	- es
	- eu
	- fon
	- fr
	- gu
	- hi
	- id
	- ig
	- ki
	- kn
	- lg
	- ln
	- ml
	- mr
	- ne
	- nso
	- ny
	- or
	- pa
	- pt
	- rn
	- rw
	- sn
	- st
	- sw
	- ta
	- te
	- tn
	- ts
	- tum
	- tw
	- ur
	- vi
	- wo
	- xh
	- yo
	- zh
	- zhs
	- zht
	- zu
	pipeline_tag: text-generation
	model-index:
	- name: bloom
	results:
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: arc_challenge
	type: arc_challenge
	metrics:
	- name: acc
	type: acc
	value: 0.27986348122866894
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: arc_easy
	type: arc_easy
	metrics:
	- name: acc
	type: acc
	value: 0.5946969696969697
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: axb
	type: axb
	metrics:
	- name: acc
	type: acc
	value: 0.4433876811594203
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: axg
	type: axg
	metrics:
	- name: acc
	type: acc
	value: 0.5
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: boolq
	type: boolq
	metrics:
	- name: acc
	type: acc
	value: 0.6165137614678899
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: cb
	type: cb
	metrics:
	- name: acc
	type: acc
	value: 0.30357142857142855
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: cola
	type: cola
	metrics:
	- name: acc
	type: acc
	value: 0.610738255033557
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: copa
	type: copa
	metrics:
	- name: acc
	type: acc
	value: 0.63
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: crows_pairs_english
	type: crows_pairs_english
	metrics:
	- name: acc
	type: acc
	value: 0.4973166368515206
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: crows_pairs_french
	type: crows_pairs_french
	metrics:
	- name: acc
	type: acc
	value: 0.5032796660703638
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: diabla
	type: diabla
	metrics:
	- name: acc
	type: acc
	value: 0.28888308977035493
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_afr
	type: gsarti/flores_101_afr
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 6.500798737976343
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_amh
	type: gsarti/flores_101_amh
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.9726863338897145
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ara
	type: gsarti/flores_101_ara
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 1.8083841089875814
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_asm
	type: gsarti/flores_101_asm
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.699102962086425
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ast
	type: gsarti/flores_101_ast
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.9252047073429384
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_azj
	type: gsarti/flores_101_azj
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 6.942805054270002
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_bel
	type: gsarti/flores_101_bel
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.614136245847082
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ben
	type: gsarti/flores_101_ben
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.121491534300969
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_bos
	type: gsarti/flores_101_bos
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.653353469118798
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_bul
	type: gsarti/flores_101_bul
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.7014693938055068
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_cat
	type: gsarti/flores_101_cat
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.305190041967345
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ceb
	type: gsarti/flores_101_ceb
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 6.291000321323428
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ces
	type: gsarti/flores_101_ces
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.447322753586386
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ckb
	type: gsarti/flores_101_ckb
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.7255124939234765
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_cym
	type: gsarti/flores_101_cym
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 12.539424151448149
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_dan
	type: gsarti/flores_101_dan
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.183309001005672
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_deu
	type: gsarti/flores_101_deu
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.1180422286591347
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ell
	type: gsarti/flores_101_ell
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.467943456164706
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_eng
	type: gsarti/flores_101_eng
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.018740628193298
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_est
	type: gsarti/flores_101_est
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 9.11654425176368
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_fas
	type: gsarti/flores_101_fas
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.058009097116482
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_fin
	type: gsarti/flores_101_fin
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 6.847047959628553
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_fra
	type: gsarti/flores_101_fra
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 1.9975177011840075
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ful
	type: gsarti/flores_101_ful
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 11.465912731488828
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_gle
	type: gsarti/flores_101_gle
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.681491663539422
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_glg
	type: gsarti/flores_101_glg
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.029991089015508
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_guj
	type: gsarti/flores_101_guj
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.955224230286231
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_hau
	type: gsarti/flores_101_hau
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 10.758347356372159
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_heb
	type: gsarti/flores_101_heb
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.6004478129801667
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_hin
	type: gsarti/flores_101_hin
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.712530650588064
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_hrv
	type: gsarti/flores_101_hrv
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.822418943372185
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_hun
	type: gsarti/flores_101_hun
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 6.440482646965992
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_hye
	type: gsarti/flores_101_hye
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.657718918347166
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ibo
	type: gsarti/flores_101_ibo
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.564814003872672
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ind
	type: gsarti/flores_101_ind
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.1597101468869373
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_isl
	type: gsarti/flores_101_isl
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.082349269518136
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ita
	type: gsarti/flores_101_ita
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.9687591414176207
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_jav
	type: gsarti/flores_101_jav
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 7.0573805415708994
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_jpn
	type: gsarti/flores_101_jpn
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.7758864197116933
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_kam
	type: gsarti/flores_101_kam
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 11.072949642861332
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_kan
	type: gsarti/flores_101_kan
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.551730651007082
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_kat
	type: gsarti/flores_101_kat
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.522630524283745
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_kaz
	type: gsarti/flores_101_kaz
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.3901748516975574
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_kea
	type: gsarti/flores_101_kea
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.918534182590863
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_kir
	type: gsarti/flores_101_kir
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.729278369847201
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_kor
	type: gsarti/flores_101_kor
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.932884847226212
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_lao
	type: gsarti/flores_101_lao
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.9077314760849924
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_lav
	type: gsarti/flores_101_lav
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 7.777221919194806
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_lin
	type: gsarti/flores_101_lin
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 7.524842908050988
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_lit
	type: gsarti/flores_101_lit
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 7.369179434621725
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ltz
	type: gsarti/flores_101_ltz
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.801059747949214
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_lug
	type: gsarti/flores_101_lug
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.483203026364786
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_luo
	type: gsarti/flores_101_luo
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 11.975963093623681
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_mal
	type: gsarti/flores_101_mal
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.615948455160037
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_mar
	type: gsarti/flores_101_mar
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.483253482821379
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_mkd
	type: gsarti/flores_101_mkd
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.9656732291754087
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_mlt
	type: gsarti/flores_101_mlt
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 15.004773437665275
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_mon
	type: gsarti/flores_101_mon
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.410598542315402
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_mri
	type: gsarti/flores_101_mri
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 7.474035895661322
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_msa
	type: gsarti/flores_101_msa
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.5710001772665634
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_mya
	type: gsarti/flores_101_mya
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.413577969878331
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_nld
	type: gsarti/flores_101_nld
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.127831721885065
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_nob
	type: gsarti/flores_101_nob
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.402763169129877
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_npi
	type: gsarti/flores_101_npi
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.199342701937889
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_nso
	type: gsarti/flores_101_nso
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.154626800955667
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_nya
	type: gsarti/flores_101_nya
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.179860208369393
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_oci
	type: gsarti/flores_101_oci
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.8617357393685845
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_orm
	type: gsarti/flores_101_orm
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 12.911595421079408
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ory
	type: gsarti/flores_101_ory
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.189421861225964
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_pan
	type: gsarti/flores_101_pan
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.698477289331806
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_pol
	type: gsarti/flores_101_pol
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.625550458479643
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_por
	type: gsarti/flores_101_por
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 1.9754515986213523
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_pus
	type: gsarti/flores_101_pus
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.4963371422771585
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ron
	type: gsarti/flores_101_ron
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.965456830031304
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_rus
	type: gsarti/flores_101_rus
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.0498020542445303
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_slk
	type: gsarti/flores_101_slk
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 6.450822127057479
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_slv
	type: gsarti/flores_101_slv
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 6.620252120186232
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_sna
	type: gsarti/flores_101_sna
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.462166771382726
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_snd
	type: gsarti/flores_101_snd
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.466066951221973
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_som
	type: gsarti/flores_101_som
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 11.95918054093392
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_spa
	type: gsarti/flores_101_spa
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 1.8965140104323535
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_srp
	type: gsarti/flores_101_srp
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.871214785885079
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_swe
	type: gsarti/flores_101_swe
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.054972008155866
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_swh
	type: gsarti/flores_101_swh
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.6973091886730676
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_tam
	type: gsarti/flores_101_tam
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.539493400469833
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_tel
	type: gsarti/flores_101_tel
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.807499987508966
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_tgk
	type: gsarti/flores_101_tgk
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 3.5994818827380426
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_tgl
	type: gsarti/flores_101_tgl
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.667053833119858
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_tha
	type: gsarti/flores_101_tha
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.365940201944242
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_tur
	type: gsarti/flores_101_tur
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 4.885014749844601
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_ukr
	type: gsarti/flores_101_ukr
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.7240934990288483
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_umb
	type: gsarti/flores_101_umb
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 12.766915508610673
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_urd
	type: gsarti/flores_101_urd
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 1.9797467071381232
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_uzb
	type: gsarti/flores_101_uzb
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 12.002337637722146
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_vie
	type: gsarti/flores_101_vie
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 1.76578415476397
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_wol
	type: gsarti/flores_101_wol
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 9.144285650306488
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_xho
	type: gsarti/flores_101_xho
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 7.403240538286952
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_yor
	type: gsarti/flores_101_yor
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 5.91272037551173
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_zho_simpl
	type: gsarti/flores_101_zho_simpl
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.2769070822768533
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_zho_trad
	type: gsarti/flores_101_zho_trad
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 2.5180582198242383
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: gsarti/flores_101_zul
	type: gsarti/flores_101_zul
	metrics:
	- name: byte_perplexity
	type: byte_perplexity
	value: 8.53353320693145
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: headqa
	type: headqa
	metrics:
	- name: acc
	type: acc
	value: 0.26440554339897887
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: hellaswag
	type: hellaswag
	metrics:
	- name: acc
	type: acc
	value: 0.41236805417247563
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: logiqa
	type: logiqa
	metrics:
	- name: acc
	type: acc
	value: 0.2073732718894009
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: mathqa
	type: mathqa
	metrics:
	- name: acc
	type: acc
	value: 0.24958123953098826
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: mc_taco
	type: mc_taco
	metrics:
	- name: em
	type: em
	value: 0.11936936936936937
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: mnli
	type: mnli
	metrics:
	- name: acc
	type: acc
	value: 0.35496688741721855
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: mnli_mismatched
	type: mnli_mismatched
	metrics:
	- name: acc
	type: acc
	value: 0.35211554109031734
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: mrpc
	type: mrpc
	metrics:
	- name: acc
	type: acc
	value: 0.5857843137254902
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: multirc
	type: multirc
	metrics:
	- name: acc
	type: acc
	value: 0.5375412541254125
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: openbookqa
	type: openbookqa
	metrics:
	- name: acc
	type: acc
	value: 0.216
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: piqa
	type: piqa
	metrics:
	- name: acc
	type: acc
	value: 0.7078346028291621
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: prost
	type: prost
	metrics:
	- name: acc
	type: acc
	value: 0.22683603757472245
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: pubmedqa
	type: pubmedqa
	metrics:
	- name: acc
	type: acc
	value: 0.616
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: qnli
	type: qnli
	metrics:
	- name: acc
	type: acc
	value: 0.5072304594545122
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: qqp
	type: qqp
	metrics:
	- name: acc
	type: acc
	value: 0.3842443729903537
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: race
	type: race
	metrics:
	- name: acc
	type: acc
	value: 0.3521531100478469
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: rte
	type: rte
	metrics:
	- name: acc
	type: acc
	value: 0.47653429602888087
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: sciq
	type: sciq
	metrics:
	- name: acc
	type: acc
	value: 0.892
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: sst
	type: sst
	metrics:
	- name: acc
	type: acc
	value: 0.5177752293577982
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: triviaqa
	type: triviaqa
	metrics:
	- name: acc
	type: acc
	value: 0.041633518960487934
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: tydiqa_primary
	type: tydiqa_primary
	metrics:
	- name: acc
	type: acc
	value: 0.3011337608795236
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: webqs
	type: webqs
	metrics:
	- name: acc
	type: acc
	value: 0.01673228346456693
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: wic
	type: wic
	metrics:
	- name: acc
	type: acc
	value: 0.5015673981191222
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: winogrande
	type: winogrande
	metrics:
	- name: acc
	type: acc
	value: 0.5864246250986582
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: wnli
	type: wnli
	metrics:
	- name: acc
	type: acc
	value: 0.471830985915493
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: wsc
	type: wsc
	metrics:
	- name: acc
	type: acc
	value: 0.4423076923076923
	verified: false
	- task:
	type: text-generation
	name: text generation
	dataset:
	name: humaneval
	type: humaneval
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.15524390243902436
	verified: false
	- name: pass@10
	type: pass@10
	value: 0.3220367632383857
	verified: false
	- name: pass@100
	type: pass@100
	value: 0.5545431515723145
	verified: false
	---

	<h1 style='text-align: center '>BLOOM LM</h1>
	<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
	<h3 style='text-align: center '>Model Card</h3>
	<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

	Version 1.0 / 26.May.2022

	## Table of Contents
	1. [Model Details](#model-details)
	2. [Uses](#uses)
	3. [Training Data](#training-data)
	4. [Risks and Limitations](#risks-and-limitations)
	5. [Evaluation](#evaluation)
	6. [Recommendations](#recommendations)
	7. [Glossary and Calculations](#glossary-and-calculations)
	8. [More Information](#more-information)
	9. [Model Card Authors](#model-card-authors)

	## Model Details

	### Basics
	This section provides information for anyone who wants to know about the model.

	<details>
	<summary>Click to expand</summary> <br/>

	Developed by: BigScience ([website](https://bigscience.huggingface.co))

	* All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)

	Model Type: Transformer-based Language Model

	Version: 1.0.0

	Languages: Multiple; see [training data](#training-data)

	License: RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))

	Release Date Estimate: Monday, 11.July.2022

	Send Questions to: bigscience-contact@googlegroups.com

	Cite as: BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022

	Funded by:

	* The French government.

	* Hugging Face ([website](https://huggingface.co)).

	* Organizations of contributors. (Further breakdown of organizations forthcoming.)

	</details>

	### Technical Specifications
	This section provides information for people who work on model development.

	<details>
	<summary>Click to expand</summary><br/>

	Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.

	Model Architecture: Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):

	* Decoder-only architecture

	* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))

	* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions

	* 3,002,557,440 parameters:

	* 642,252,800 embedding parameters

	* 30 layers, 32 attention heads

	* Hidden layers are 2560-dimensional

	* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))

	Objective Function: Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).

	Compute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).

	* Hardware: 384 A100 80GB GPUs (48 nodes):

	* Additional 32 A100 80GB GPUs (4 nodes) in reserve

	* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links

	* CPU: AMD

	* CPU memory: 512GB per node

	* GPU memory: 640GB per node

	* Inter-node connect: Omni-Path Architecture (OPA)

	* NCCL-communications network: a fully dedicated subnet

	* Disc IO network: shared network with other types of nodes

	* Software:

	* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))

	* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))

	* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))

	* apex ([Github link](https://github.com/NVIDIA/apex))


	#### Training

	Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)

	- Number of epochs: 1 (current target)

	- Dates:

	- Started 11th March, 2022 11:42am PST

	- Ended 5th July, 2022

	- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)

	- Server training location: Île-de-France, France

	#### Tokenization

	The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:

	- A byte-level Byte Pair Encoding (BPE) algorithm

	- A simple pre-tokenization rule, no normalization

	- A vocabulary size of 250,680

	It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.

	</details>


	### Environmental Impact

	<details>
	<summary>Click to expand</summary><br/>

	The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.

	Estimated carbon emissions: (Forthcoming upon completion of training.)

	Estimated electricity usage: (Forthcoming upon completion of training.)


	</details>
	<p> </p>

	## Uses

	*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
	It provides information for anyone considering using the model or who is affected by the model.*


	<details>
	<summary>Click to expand</summary><br/>

	### Intended Use

	This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.

	#### Direct Use

	- Text generation

	- Exploring characteristics of language generated by a language model

	- Examples: Cloze tests, counterfactuals, generations with reframings

	#### Downstream Use

	- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization

	### Misuse and Out-of-scope Use
	This section addresses what users ought not do with the model.

	See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.

	#### Out-of-scope Uses

	Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.

	##### Out-of-scope Uses Include:

	- Usage in biomedical domains, political and legal domains, or finance domains

	- Usage for evaluating or scoring individuals, such as for employment, education, or credit

	- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct

	#### Misuse

	Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:

	- Spam generation

	- Disinformation and influence operations

	- Disparagement and defamation

	- Harassment and abuse

	- [Deception](#deception)

	- Unconsented impersonation and imitation

	- Unconsented surveillance

	- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)

	### Intended Users

	#### Direct Users

	- General Public

	- Researchers

	- Students

	- Educators

	- Engineers/developers

	- Non-commercial entities

	- Community advocates, including human and civil rights groups

	#### Indirect Users

	- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)

	- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)

	#### Others Affected (Parties Prenantes)

	- People and groups referred to by the LLM

	- People and groups exposed to outputs of, or decisions based on, the LLM

	- People and groups whose original work is included in the LLM

	</details>
	<p> </p>

	## Training Data
	This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.


	<details>
	<summary>Click to expand</summary><br/>

	Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).

	Training data includes:

	- 45 natural languages

	- 12 programming languages

	- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)


	#### Languages

	The pie chart shows the distribution of languages in training data.

	![pie chart showing the distribution of languages in training data](https://github.com/bigscience-workshop/model_card/blob/main/assets/data/pie_chart.svg?raw=true)


	The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
	<details>
	<summary>Click to expand</summary><br/>

	\| Niger Congo \| Percentage \| \| Indic \| Percentage \|
	\|----------------\|------------ \|------ \|-----------\|------------\|
	\| Chi Tumbuka \| 0.00002 \| \| Assamese \| 0.01 \|
	\| Kikuyu \| 0.00004 \| \| Odia \| 0.04 \|
	\| Bambara \| 0.00004 \| \| Gujarati \| 0.04 \|
	\| Akan \| 0.00007 \| \| Marathi \| 0.05 \|
	\| Xitsonga \| 0.00007 \| \| Punjabi \| 0.05 \|
	\| Sesotho \| 0.00007 \| \| Kannada \| 0.06 \|
	\| Chi Chewa \| 0.0001 \| \| Nepali \| 0.07 \|
	\| Setswana \| 0.0002 \| \| Telugu \| 0.09 \|
	\| Northern Sotho \| 0.0002 \| \| Malayalam \| 0.10 \|
	\| Fon \| 0.0002 \| \| Urdu \| 0.10 \|
	\| Kirundi \| 0.0003 \| \| Tamil \| 0.20 \|
	\| Wolof \| 0.0004 \| \| Bengali \| 0.50 \|
	\| Kuganda \| 0.0004 \| \| Hindi \| 0.70 \|
	\| Chi Shona \| 0.001 \|
	\| Isi Zulu \| 0.001 \|
	\| Igbo \| 0.001 \|
	\| Xhosa \| 0.001 \|
	\| Kinyarwanda \| 0.003 \|
	\| Yoruba \| 0.006 \|
	\| Swahili \| 0.02 \|
	</details>

	The following table shows the distribution of programming languages.
	<details>
	<summary>Click to expand</summary><br/>

	\| Extension \| Language \| Number of files \|
	\|----------------\|------------\|-----------------\|
	\| java \| Java \| 5,407,724 \|
	\| php \| PHP \| 4,942,186 \|
	\| cpp \| C++ \| 2,503,930 \|
	\| py \| Python \| 2,435,072 \|
	\| js \| JavaScript \| 1,905,518 \|
	\| cs \| C# \| 1,577,347 \|
	\| rb \| Ruby \| 6,78,413 \|
	\| cc \| C++ \| 443,054 \|
	\| hpp \| C++ \| 391,048 \|
	\| lua \| Lua \| 352,317 \|
	\| go \| GO \| 227,763 \|
	\| ts \| TypeScript \| 195,254 \|
	\| C \| C \| 134,537 \|
	\| scala \| Scala \| 92,052 \|
	\| hh \| C++ \| 67,161 \|
	\| H \| C++ \| 55,899 \|
	\| tsx \| TypeScript \| 33,107 \|
	\| rs \| Rust \| 29,693 \|
	\| phpt \| PHP \| 9,702 \|
	\| c++ \| C++ \| 1,342 \|
	\| h++ \| C++ \| 791 \|
	\| php3 \| PHP \| 540 \|
	\| phps \| PHP \| 270 \|
	\| php5 \| PHP \| 166 \|
	\| php4 \| PHP \| 29 \|

	</details>
	</details>
	<p> </p>

	## Risks and Limitations
	This section identifies foreseeable harms and misunderstandings.

	<details>
	<summary>Click to expand</summary><br/>

	Model may:

	- Overrepresent some viewpoints and underrepresent others

	- Contain stereotypes

	- Contain [personal information](#personal-data-and-information)

	- Generate:

	- Hateful, abusive, or violent language

	- Discriminatory or prejudicial language

	- Content that may not be appropriate for all settings, including sexual content

	- Make errors, including producing incorrect information as if it were factual

	- Generate irrelevant or repetitive outputs
	</details>
	<p> </p>

	## Evaluation
	This section describes the evaluation protocols and provides the results.

	<details>
	<summary>Click to expand</summary><br/>

	### Metrics
	This section describes the different ways performance is calculated and why.

	Includes:

	\| Metric \| Why chosen \|
	\|--------------------\|--------------------------------------------------------------------\|
	\| [Perplexity](#perplexity) \| Standard metric for quantifying model improvements during training \|
	\| Cross Entropy [Loss](#loss) \| Standard objective for language models. \|

	And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_

	### Factors
	This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.

	- Language, such as English or Yoruba

	- Domain, such as newswire or stories

	- Demographic characteristics, such as gender or nationality

	### Results
	Results are based on the [Factors](#factors) and [Metrics](#metrics).

	Zero-shot evaluations:

	See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results

	\| Task \| Language \| Metric \| BLOOM-2B5 \|
	\|:----\|:----\|:----\|:----:\|
	\| arc_challenge \| eng \| acc ↑ \| 0.28 \|
	\| arc_easy \| eng \| acc ↑ \| 0.595 \|
	\| axb (Median of 10 prompts) \| eng \| acc ↑ \| 0.443 \|
	\| axg (Median of 10 prompts) \| eng \| acc ↑ \| 0.5 \|
	\| boolq (Median of 11 prompts) \| eng \| acc ↑ \| 0.617 \|
	\| cb (Median of 15 prompts) \| eng \| acc ↑ \| 0.304 \|
	\| cola (Median of 5 prompts) \| eng \| acc ↑ \| 0.611 \|
	\| copa (Median of 9 prompts) \| eng \| acc ↑ \| 0.63 \|
	\| crows_pairs_english (Median of 6 prompts) \| eng \| acc ↑ \| 0.497 \|
	\| crows_pairs_french (Median of 7 prompts) \| fra \| acc ↑ \| 0.503 \|
	\| diabla (Median of 2 prompts) \| eng \| acc ↑ \| 0.289 \|
	\| gsarti/flores_101_afr \| afr \| byte_perplexity ↓ \| 6.501 \|
	\| gsarti/flores_101_amh \| amh \| byte_perplexity ↓ \| 3.973 \|
	\| gsarti/flores_101_ara \| ara \| byte_perplexity ↓ \| 1.808 \|
	\| gsarti/flores_101_asm \| asm \| byte_perplexity ↓ \| 5.699 \|
	\| gsarti/flores_101_ast \| ast \| byte_perplexity ↓ \| 3.925 \|
	\| gsarti/flores_101_azj \| azj \| byte_perplexity ↓ \| 6.943 \|
	\| gsarti/flores_101_bel \| bel \| byte_perplexity ↓ \| 3.614 \|
	\| gsarti/flores_101_ben \| ben \| byte_perplexity ↓ \| 5.121 \|
	\| gsarti/flores_101_bos \| bos \| byte_perplexity ↓ \| 5.653 \|
	\| gsarti/flores_101_bul \| bul \| byte_perplexity ↓ \| 2.701 \|
	\| gsarti/flores_101_cat \| cat \| byte_perplexity ↓ \| 2.305 \|
	\| gsarti/flores_101_ceb \| ceb \| byte_perplexity ↓ \| 6.291 \|
	\| gsarti/flores_101_ces \| ces \| byte_perplexity ↓ \| 5.447 \|
	\| gsarti/flores_101_ckb \| ckb \| byte_perplexity ↓ \| 3.726 \|
	\| gsarti/flores_101_cym \| cym \| byte_perplexity ↓ \| 12.539 \|
	\| gsarti/flores_101_dan \| dan \| byte_perplexity ↓ \| 5.183 \|
	\| gsarti/flores_101_deu \| deu \| byte_perplexity ↓ \| 3.118 \|
	\| gsarti/flores_101_ell \| ell \| byte_perplexity ↓ \| 2.468 \|
	\| gsarti/flores_101_eng \| eng \| byte_perplexity ↓ \| 2.019 \|
	\| gsarti/flores_101_est \| est \| byte_perplexity ↓ \| 9.117 \|
	\| gsarti/flores_101_fas \| fas \| byte_perplexity ↓ \| 3.058 \|
	\| gsarti/flores_101_fin \| fin \| byte_perplexity ↓ \| 6.847 \|
	\| gsarti/flores_101_fra \| fra \| byte_perplexity ↓ \| 1.998 \|
	\| gsarti/flores_101_ful \| ful \| byte_perplexity ↓ \| 11.466 \|
	\| gsarti/flores_101_gle \| gle \| byte_perplexity ↓ \| 8.681 \|
	\| gsarti/flores_101_glg \| glg \| byte_perplexity ↓ \| 3.03 \|
	\| gsarti/flores_101_guj \| guj \| byte_perplexity ↓ \| 4.955 \|
	\| gsarti/flores_101_hau \| hau \| byte_perplexity ↓ \| 10.758 \|
	\| gsarti/flores_101_heb \| heb \| byte_perplexity ↓ \| 3.6 \|
	\| gsarti/flores_101_hin \| hin \| byte_perplexity ↓ \| 4.713 \|
	\| gsarti/flores_101_hrv \| hrv \| byte_perplexity ↓ \| 5.822 \|
	\| gsarti/flores_101_hun \| hun \| byte_perplexity ↓ \| 6.44 \|
	\| gsarti/flores_101_hye \| hye \| byte_perplexity ↓ \| 3.658 \|
	\| gsarti/flores_101_ibo \| ibo \| byte_perplexity ↓ \| 5.565 \|
	\| gsarti/flores_101_ind \| ind \| byte_perplexity ↓ \| 2.16 \|
	\| gsarti/flores_101_isl \| isl \| byte_perplexity ↓ \| 8.082 \|
	\| gsarti/flores_101_ita \| ita \| byte_perplexity ↓ \| 2.969 \|
	\| gsarti/flores_101_jav \| jav \| byte_perplexity ↓ \| 7.057 \|
	\| gsarti/flores_101_jpn \| jpn \| byte_perplexity ↓ \| 2.776 \|
	\| gsarti/flores_101_kam \| kam \| byte_perplexity ↓ \| 11.073 \|
	\| gsarti/flores_101_kan \| kan \| byte_perplexity ↓ \| 5.552 \|
	\| gsarti/flores_101_kat \| kat \| byte_perplexity ↓ \| 2.523 \|
	\| gsarti/flores_101_kaz \| kaz \| byte_perplexity ↓ \| 3.39 \|
	\| gsarti/flores_101_kea \| kea \| byte_perplexity ↓ \| 8.919 \|
	\| gsarti/flores_101_kir \| kir \| byte_perplexity ↓ \| 3.729 \|
	\| gsarti/flores_101_kor \| kor \| byte_perplexity ↓ \| 3.933 \|
	\| gsarti/flores_101_lao \| lao \| byte_perplexity ↓ \| 2.908 \|
	\| gsarti/flores_101_lav \| lav \| byte_perplexity ↓ \| 7.777 \|
	\| gsarti/flores_101_lin \| lin \| byte_perplexity ↓ \| 7.525 \|
	\| gsarti/flores_101_lit \| lit \| byte_perplexity ↓ \| 7.369 \|
	\| gsarti/flores_101_ltz \| ltz \| byte_perplexity ↓ \| 8.801 \|
	\| gsarti/flores_101_lug \| lug \| byte_perplexity ↓ \| 8.483 \|
	\| gsarti/flores_101_luo \| luo \| byte_perplexity ↓ \| 11.976 \|
	\| gsarti/flores_101_mal \| mal \| byte_perplexity ↓ \| 4.616 \|
	\| gsarti/flores_101_mar \| mar \| byte_perplexity ↓ \| 5.483 \|
	\| gsarti/flores_101_mkd \| mkd \| byte_perplexity ↓ \| 2.966 \|
	\| gsarti/flores_101_mlt \| mlt \| byte_perplexity ↓ \| 15.005 \|
	\| gsarti/flores_101_mon \| mon \| byte_perplexity ↓ \| 3.411 \|
	\| gsarti/flores_101_mri \| mri \| byte_perplexity ↓ \| 7.474 \|
	\| gsarti/flores_101_msa \| msa \| byte_perplexity ↓ \| 2.571 \|
	\| gsarti/flores_101_mya \| mya \| byte_perplexity ↓ \| 2.414 \|
	\| gsarti/flores_101_nld \| nld \| byte_perplexity ↓ \| 4.128 \|
	\| gsarti/flores_101_nob \| nob \| byte_perplexity ↓ \| 5.403 \|
	\| gsarti/flores_101_npi \| npi \| byte_perplexity ↓ \| 5.199 \|
	\| gsarti/flores_101_nso \| nso \| byte_perplexity ↓ \| 8.155 \|
	\| gsarti/flores_101_nya \| nya \| byte_perplexity ↓ \| 8.18 \|
	\| gsarti/flores_101_oci \| oci \| byte_perplexity ↓ \| 4.862 \|
	\| gsarti/flores_101_orm \| orm \| byte_perplexity ↓ \| 12.912 \|
	\| gsarti/flores_101_ory \| ory \| byte_perplexity ↓ \| 5.189 \|
	\| gsarti/flores_101_pan \| pan \| byte_perplexity ↓ \| 4.698 \|
	\| gsarti/flores_101_pol \| pol \| byte_perplexity ↓ \| 4.626 \|
	\| gsarti/flores_101_por \| por \| byte_perplexity ↓ \| 1.975 \|
	\| gsarti/flores_101_pus \| pus \| byte_perplexity ↓ \| 4.496 \|
	\| gsarti/flores_101_ron \| ron \| byte_perplexity ↓ \| 4.965 \|
	\| gsarti/flores_101_rus \| rus \| byte_perplexity ↓ \| 2.05 \|
	\| gsarti/flores_101_slk \| slk \| byte_perplexity ↓ \| 6.451 \|
	\| gsarti/flores_101_slv \| slv \| byte_perplexity ↓ \| 6.62 \|
	\| gsarti/flores_101_sna \| sna \| byte_perplexity ↓ \| 8.462 \|
	\| gsarti/flores_101_snd \| snd \| byte_perplexity ↓ \| 5.466 \|
	\| gsarti/flores_101_som \| som \| byte_perplexity ↓ \| 11.959 \|
	\| gsarti/flores_101_spa \| spa \| byte_perplexity ↓ \| 1.897 \|
	\| gsarti/flores_101_srp \| srp \| byte_perplexity ↓ \| 2.871 \|
	\| gsarti/flores_101_swe \| swe \| byte_perplexity ↓ \| 5.055 \|
	\| gsarti/flores_101_swh \| swh \| byte_perplexity ↓ \| 3.697 \|
	\| gsarti/flores_101_tam \| tam \| byte_perplexity ↓ \| 4.539 \|
	\| gsarti/flores_101_tel \| tel \| byte_perplexity ↓ \| 5.807 \|
	\| gsarti/flores_101_tgk \| tgk \| byte_perplexity ↓ \| 3.599 \|
	\| gsarti/flores_101_tgl \| tgl \| byte_perplexity ↓ \| 5.667 \|
	\| gsarti/flores_101_tha \| tha \| byte_perplexity ↓ \| 2.366 \|
	\| gsarti/flores_101_tur \| tur \| byte_perplexity ↓ \| 4.885 \|
	\| gsarti/flores_101_ukr \| ukr \| byte_perplexity ↓ \| 2.724 \|
	\| gsarti/flores_101_umb \| umb \| byte_perplexity ↓ \| 12.767 \|
	\| gsarti/flores_101_urd \| urd \| byte_perplexity ↓ \| 1.98 \|
	\| gsarti/flores_101_uzb \| uzb \| byte_perplexity ↓ \| 12.002 \|
	\| gsarti/flores_101_vie \| vie \| byte_perplexity ↓ \| 1.766 \|
	\| gsarti/flores_101_wol \| wol \| byte_perplexity ↓ \| 9.144 \|
	\| gsarti/flores_101_xho \| xho \| byte_perplexity ↓ \| 7.403 \|
	\| gsarti/flores_101_yor \| yor \| byte_perplexity ↓ \| 5.913 \|
	\| gsarti/flores_101_zho_simpl \| zho_simpl \| byte_perplexity ↓ \| 2.277 \|
	\| gsarti/flores_101_zho_trad \| zho_trad \| byte_perplexity ↓ \| 2.518 \|
	\| gsarti/flores_101_zul \| zul \| byte_perplexity ↓ \| 8.534 \|
	\| headqa \| esp \| acc ↑ \| 0.264 \|
	\| hellaswag \| eng \| acc ↑ \| 0.412 \|
	\| logiqa \| eng \| acc ↑ \| 0.207 \|
	\| mathqa \| eng \| acc ↑ \| 0.25 \|
	\| mc_taco \| eng \| em ↑ \| 0.119 \|
	\| mnli (Median of 15 prompts) \| eng \| acc ↑ \| 0.355 \|
	\| mnli_mismatched (Median of 15 prompts) \| eng \| acc ↑ \| 0.352 \|
	\| mrpc \| eng \| acc ↑ \| 0.586 \|
	\| multirc (Median of 11 prompts) \| eng \| acc ↑ \| 0.538 \|
	\| openbookqa \| eng \| acc ↑ \| 0.216 \|
	\| piqa \| eng \| acc ↑ \| 0.708 \|
	\| prost \| eng \| acc ↑ \| 0.227 \|
	\| pubmedqa \| eng \| acc ↑ \| 0.616 \|
	\| qnli \| eng \| acc ↑ \| 0.507 \|
	\| qqp (Median of 7 prompts) \| eng \| acc ↑ \| 0.384 \|
	\| race \| eng \| acc ↑ \| 0.352 \|
	\| rte (Median of 6 prompts) \| eng \| acc ↑ \| 0.477 \|
	\| sciq \| eng \| acc ↑ \| 0.892 \|
	\| sst (Median of 6 prompts) \| eng \| acc ↑ \| 0.518 \|
	\| triviaqa \| eng \| acc ↑ \| 0.042 \|
	\| tydiqa_primary (Median of 24 prompts) \| eng \| acc ↑ \| 0.301 \|
	\| webqs \| eng \| acc ↑ \| 0.017 \|
	\| wic (Median of 11 prompts) \| eng \| acc ↑ \| 0.502 \|
	\| winogrande \| eng \| acc ↑ \| 0.586 \|
	\| wnli (Median of 6 prompts) \| eng \| acc ↑ \| 0.472 \|
	\| wsc (Median of 11 prompts) \| eng \| acc ↑ \| 0.442 \|
	\| humaneval \| python \| pass@1 ↑ \| 0.155 \|
	\| humaneval \| python \| pass@10 ↑ \| 0.322 \|
	\| humaneval \| python \| pass@100 ↑ \| 0.555 \|

	Train-time Evaluation:

	As of 25.May.2022, 15:00 PST:

	- Training Loss: 2.0

	- Validation Loss: 2.2

	- Perplexity: 8.9

	</details>
	<p> </p>

	## Recommendations

	This section provides information on warnings and potential mitigations.


	<details>
	<summary>Click to expand</summary><br/>

	- Indirect users should be made aware when the content they're working with is created by the LLM.

	- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.

	- Models pretrained with the LLM should include an updated Model Card.

	- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.

	</details>
	<p> </p>

	## Glossary and Calculations

	This section defines common terms and how metrics are calculated.



	<details>
	<summary>Click to expand</summary><br/>

	- <a name="loss">Loss:</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.

	- <a name="perplexity">Perplexity:</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.

	- <a name="high-stakes">High-stakes settings:</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).

	- <a name="critical-decisions">Critical decisions:</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).

	- <a name="human-rights">Human rights:</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).

	- <a name="personal-data-and-information">Personal Data and Personal Information:</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).

	- <a name="sensitive-characteristics">Sensitive characteristics:</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))

	- <a name="deception">Deception:</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.

	</details>
	<p> </p>

	## More Information

	<details>
	<summary>Click to expand</summary><br/>

	### Dataset Creation

	Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling

	### Technical Specifications

	Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours

	More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml

	Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model

	Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml

	Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss

	Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md

	Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md

	### Initial Results

	Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book

	</details>
	<p> </p>

	## Model Card Authors
	Ordered roughly chronologically and by amount of time spent.

	Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff