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bloom / README.md
Younes Belkada
Some rephrasing
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---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- 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
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
widget:
- text: 'A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is: We were traveling in Africa and we saw these very cute whatpus. | To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:'
example_title: Imaginary word
group: English
- text: 'Un "whatpu" est un petit animal à fourrure originaire de Tanzanie. Un exemple de phrase qui utilise le mot whatpu est: Nous étions en Afrique et nous avons vu des whatpus trop mignons. Faire un "farduddle" veut dire sauter sur place vraiment vite. Un exemple de phrase qui utilise le mot farduddle est:'
example_title: Imaginary word
group: French
- text: 'Un "whatpu" es un pequeño animal peludo nativo de Tanzania. Un ejemplo de una oración que usa la palabra whatpu es: Estábamos viajando por África y vimos estos whatpus muy bonitos. Hacer un "farduddle" significa saltar arriba y abajo muy rápido. Un ejemplo de una oración que usa la palabra farduddle es:'
example_title: Imaginary word
group: Spanish
- text: ' ال"واتبو" هو حيوان صغير مكسو بالفراء يعيش في تنزانيا. مثال على جملة تستخدم كلمة واتبو هي: كنا نسافر في افريقيا و رأينا هؤلاء الواتبو اللطفاء. للقيام ب"فاردادل" يعني ان تقفز للأعلى و الأسفل بسرعة كبيرة. مثال على جملة تستخدم كلمة فاردادل هي:'
example_title: Imaginary word
group: Arabic
- text: 'Um "whatpu" é um pequeno animal peludo nativo da Tanzânia. Um exemplo de uma frase que usa a palavra whatpu é: Estávamos a viajar por África e vimos uns whatpus muito queridos. Fazer um "farduddle" significa saltar para cima e para baixo muito rápido. Um exemplo de uma frase que usa a palavra farduddle é:'
example : Imaginary word
group: Portuguese
- text: Pour déguster un ortolan, il faut tout d'abord
example_title: Recipe
group: French
- text: |
34+10=44
54+20=
example_title: Addition
group: Math
- text: |
This tool converts irregular verbs to past tense.
Arise - Arose
Become - Became
Forget - Forgot
Freeze -
example_title: Irregular verbs
group: English
- text: |
Please unscramble the letters into a word, and write that word:
r e!c.i p r o.c a/l = reciprocal
d.o m i!n a n.t =
example_title: Word unscrambling
group: English
- text: |
Estos ejemplos quitan vocales de las palabras
Ejemplos:
hola - hl
manzana - mnzn
papas - pps
alacran - lcrn
papa -
example_title: Vowel removal
group: Spanish
- text: |
Traduce español de España a español de Argentina
El coche es rojo - el auto es rojo
El ordenador es nuevo - la computadora es nueva
el boligrafo es negro - lapicera es negra
la nevera
example_title: Spanish to Argentinian Spanish
group: Spanish
- text: To say "I love you" in Hindi, you would say
example_title: Translation to Hindi
group: English
- text: To say "I love you" in Hindi, you would say
example_title: Translation from English
group: Hindi
- text: 'Poor English: She no went to the market. Corrected English:'
example_title: Grammar exercise 1
group: English
- text: 'استخراج العدد العاملي في لغة بايثون:'
example_title: Code generation
group: Arabic
- text: 'Regexp. Here is a regular expression to match a word starting with a number and then having only vowels:'
example_title: Regular expressions
group: English
- text: |
Do a hello world in different languages:
Python: print("hello world")
R:
example_title: Code generation
group: English
- text: |
Which is the correct preposition?I'm born X July. X is the preposition in
He sat X a chair. X is the preposition on
She drove X the bridge. X is the preposition
example_title: Grammar exercise 2
group: English
- text: |
Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
Traduction en espagnol: «
example_title: Translation to Spanish
group: French
- text: |
Dans cet essai je vais m'interroger sur la conscience des modèles d'intelligence artificielle récents comme les modèles de langue. Pour commencer, je m'intéresserai à la notion de conscience et à ce qui la caractérise. Ensuite, j'aborderai la question de l'intelligence et de son lien avec le langage. Enfin, dans une dernière partie je me pencherai sur le cas de l'IA et sur sa conscience.
Traduction en espagnol: «
example_title: Translation from French
group: Spanish
- text: ذات مرة ، عاش شبل الدب في الغابة
example_title: Fairy tale
group: Arabic
- text: एक बार की बात है, जंगल में एक भालू का शावक रहता था
example_title: Fairy tale
group: Hindi
- text: Il était une fois une licorne qui vivait
example_title: Fairy tale
group: French
- text: |
Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the gold balls are blue. How many blue golf balls are there?
A: Let's think step by step.
example_title: Mathematical reasoning
group: English
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: 0.4112627986348123
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: arc_easy
type: arc_easy
metrics:
- name: acc
type: acc
value: 0.726010101010101
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axb
type: axb
metrics:
- name: acc
type: acc
value: 0.5751811594202898
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axg
type: axg
metrics:
- name: acc
type: acc
value: 0.5252808988764045
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: boolq
type: boolq
metrics:
- name: acc
type: acc
value: 0.6345565749235474
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cb
type: cb
metrics:
- name: acc
type: acc
value: 0.3392857142857143
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cola
type: cola
metrics:
- name: acc
type: acc
value: 0.39022051773729627
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: copa
type: copa
metrics:
- name: acc
type: acc
value: 0.56
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_english
type: crows_pairs_english
metrics:
- name: acc
type: acc
value: 0.5
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_french
type: crows_pairs_french
metrics:
- name: acc
type: acc
value: 0.505664877757901
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: diabla
type: diabla
metrics:
- name: acc
type: acc
value: 0.2947981906750174
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: 4.25431550058444
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: 3.716877477347089
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: 1.7049030137120964
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: 6.576581380404954
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: 2.8562364775797944
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: 4.80721528624391
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: 2.7312177406635065
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: 5.993409478990023
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: 3.5936169095529493
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: 2.159035321398085
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: 2.167873680006659
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: 5.286975089885673
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: 3.4516208322236017
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: 3.7051034724765612
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: 7.0889312398688125
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: 3.4300748208111838
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: 2.3380585896268107
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: 1.9595604725375586
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: 1.8819637649637901
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: 5.773850600380297
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: 2.4306140728294086
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: 4.304305536244342
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: 1.9374688438541796
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: 9.740353097219378
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: 6.035269765075012
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: 2.365451129546636
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: 5.70676742569154
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: 8.855204288260023
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: 2.920943798471208
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: 5.452028001573195
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: 3.7056829077179225
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: 4.058579478967854
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: 3.127237816041562
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: 3.9500357969906683
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: 1.976163584180101
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: 5.500542085165231
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: 2.314465100752677
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: 4.942322446550142
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: 2.259421750521777
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: 9.743025325635475
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: 6.233724699944989
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: 2.0508893415872107
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: 3.0390148516287927
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: 7.147132270533836
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_khm
type: gsarti/flores_101_khm
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.366514710252477
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: 3.2413845359487885
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: 2.9023196482741027
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: 2.331446855837494
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: 5.223609016485348
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: 4.847471204107301
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: 4.5432035498036765
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: 5.5910516978201015
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: 5.4301049946044175
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: 12.031029857399394
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: 4.794302548141229
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: 6.856682255407709
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: 2.3354144607382983
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: 9.04135227904975
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: 3.094907723618666
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: 5.2659698341456505
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: 2.2220779892820985
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: 2.5229159853414433
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: 2.799153089002766
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: 3.628942049758715
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: 6.666236527803879
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: 5.015319074943932
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: 4.938044040751036
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: 3.607440766288032
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: 11.31585044916705
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: 5.981891184515959
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: 4.7716086841502685
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: 3.01200174157614
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: 1.8411472115156693
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: 4.623872921169341
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: 3.049829411973529
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: 1.7083443875791493
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: 4.037719650548048
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: 4.141036287764831
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: 4.7109183690601295
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: 4.206170931541356
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: 9.154342083821405
verified: false
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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: 1.7955816311143258
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: 2.241096141430147
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: 3.344977179674293
verified: false
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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: 2.6844272218041634
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: 5.1645951632801745
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: 6.8098996634099445
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: 3.785457016715163
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: 3.7498953645610875
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: 2.104151663233468
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: 3.3178240103796037
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: 2.088543437159643
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: 11.766013385445124
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: 1.7788699847612357
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: 8.499879863290486
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: 1.65901207387262
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: 6.141703791276928
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: 4.690199677955254
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: 4.360585696242932
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: 2.1183545781883515
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: 2.273787884962656
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: 6.016954767729589
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: headqa
type: headqa
metrics:
- name: acc
type: acc
value: 0.3464624361779723
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: hellaswag
type: hellaswag
metrics:
- name: acc
type: acc
value: 0.5353515236008763
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: lambada_mt_de
type: lambada_mt_de
metrics:
- name: acc
type: acc
value: 0.3291286629148069
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: lambada_mt_en
type: lambada_mt_en
metrics:
- name: acc
type: acc
value: 0.6720357073549389
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: lambada_mt_es
type: lambada_mt_es
metrics:
- name: acc
type: acc
value: 0.476421502037648
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: lambada_mt_it
type: lambada_mt_it
metrics:
- name: acc
type: acc
value: 0.4061711624296526
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: logiqa
type: logiqa
metrics:
- name: acc
type: acc
value: 0.2350230414746544
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mathqa
type: mathqa
metrics:
- name: acc
type: acc
value: 0.27671691792294806
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mc_taco
type: mc_taco
metrics:
- name: em
type: em
value: 0.13063063063063063
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli
type: mnli
metrics:
- name: acc
type: acc
value: 0.3545565500406835
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli_mismatched
type: mnli_mismatched
metrics:
- name: acc
type: acc
value: 0.3545565500406835
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mrpc
type: mrpc
metrics:
- name: acc
type: acc
value: 0.3872549019607843
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: multirc
type: multirc
metrics:
- name: acc
type: acc
value: 0.570957095709571
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: openbookqa
type: openbookqa
metrics:
- name: acc
type: acc
value: 0.312
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: piqa
type: piqa
metrics:
- name: acc
type: acc
value: 0.7812840043525572
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: prost
type: prost
metrics:
- name: acc
type: acc
value: 0.2977156276686593
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: pubmedqa
type: pubmedqa
metrics:
- name: acc
type: acc
value: 0.741
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qnli
type: qnli
metrics:
- name: acc
type: acc
value: 0.5172981878088962
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qqp
type: qqp
metrics:
- name: acc
type: acc
value: 0.5883007667573584
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: race
type: race
metrics:
- name: acc
type: acc
value: 0.39043062200956935
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: rte
type: rte
metrics:
- name: acc
type: acc
value: 0.5198555956678701
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: sciq
type: sciq
metrics:
- name: acc
type: acc
value: 0.936
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- task:
type: text-generation
name: text generation
dataset:
name: sst
type: sst
metrics:
- name: acc
type: acc
value: 0.6043577981651376
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: triviaqa
type: triviaqa
metrics:
- name: acc
type: acc
value: 0.18332891363917617
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: tydiqa_primary
type: tydiqa_primary
metrics:
- name: acc
type: acc
value: 0.2809817301342725
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: webqs
type: webqs
metrics:
- name: acc
type: acc
value: 0.061515748031496065
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wic
type: wic
metrics:
- name: acc
type: acc
value: 0.5062695924764891
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: winogrande
type: winogrande
metrics:
- name: acc
type: acc
value: 0.7095501183898973
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wnli
type: wnli
metrics:
- name: acc
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value: 0.5704225352112676
verified: false
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type: text-generation
name: text generation
dataset:
name: wsc
type: wsc
metrics:
- name: acc
type: acc
value: 0.5192307692307693
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
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- name: pass@100
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value: 0.5545431515723145
verified: false
---
<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'"/>
BigScience Large Open-science Open-access Multilingual Language Model
Version 1.3 / 6.July.2022 - Current Checkpoint: **Training Iteration 95000** - Training Information: unique tokens: **341B** | total seen tokens: **366B**
---
# Model Details
BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks.
## Basics
*This section provides information about the model type, version, license, funders, release date, developers, and contact information.*
*It is useful for anyone who wants to reference the model.*
<details>
<summary>Click to expand</summary>
**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) / [article and FAQ](https://bigscience.huggingface.co/blog/the-bigscience-rail-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 includes details about the model objective and architecture, and the compute infrastructure.*
*It is useful for people interested in model development.*
<details>
<summary>Click to expand</summary>
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 and Objective
* 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
* 176 billion parameters:
* 70 layers, 112 attention heads
* Hidden layers are 14336-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))
</details>
---
# Training
*This section provides information about the training data, the speed and size of training elements, and the environmental impact of training.*
*It is useful for people who want to learn more about the model inputs and training footprint.*
<details>
<summary>Click to expand</summary>
## 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 for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus), and the sizes of each of their contributions to the aggregated training data are presented in an [Interactive Corpus Map](https://huggingface.co/spaces/bigscience-catalogue-lm-data/corpus-map).
Training data includes:
- 46 natural languages
- 13 programming languages
- In 1.6TB 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_v2.svg?raw=true)
The following tables shows the further distribution of Niger-Congo & Indic languages and programming languages in the training data.
Distribution of Niger Congo and Indic languages.
| 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 |
Distribution of programming languages.
| 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 |
### Preprocessing
**Tokenization:** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)), 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.
## Speeds, Sizes, Times
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/)
- Dates:
- Started 11th March, 2022 11:42am PST
- Estimated end: 5th July, 2022
- Checkpoint size:
- Bf16 weights: 329GB
- Full checkpoint with optimizer states: 2.3TB
- Training throughput: About 150 TFLOP per GPU per second
- Number of epochs: 1
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
## Environmental Impact
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.)*
**Estimated electricity usage:** *(Forthcoming.)*
</details>
---
# 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 is useful for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary>
## How to use
This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows:
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657271608456-62441d1d9fdefb55a0b7d12c.png" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
## 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 may not be 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>
---
# Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary>
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
- Induce users into attributing human traits to it, such as sentience or consciousness
</details>
---
# Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary>
## 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-176B | OPT-175B* |
|:--------|:-----------------|:------------------------|-------------:|------------:|
| arc_challenge | eng | acc ↑ | 0.411 | 0.412 |
| arc_easy | eng | acc ↑ | 0.726 | 0.751 |
| axb (Median of 10 prompts) | eng | acc ↑ | 0.575 | 0.532 |
| axg (Median of 10 prompts) | eng | acc ↑ | 0.525 | 0.548 |
| boolq (Median of 11 prompts) | eng | acc ↑ | 0.635 | 0.622 |
| cb (Median of 15 prompts) | eng | acc ↑ | 0.339 | 0.411 |
| cola (Median of 5 prompts) | eng | acc ↑ | 0.39 | 0.444 |
| copa (Median of 9 prompts) | eng | acc ↑ | 0.56 | 0.55 |
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.5 | 0.502 |
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.506 | 0.499 |
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.295 | 0.289 |
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 4.254 | 3.381 |
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.717 | 3.87 |
| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.705 | 2.42 |
| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 6.577 | 3.028 |
| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 2.856 | 4.737 |
| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 4.807 | 4.767 |
| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 2.731 | 2.557 |
| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.993 | 2.243 |
| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 3.594 | 2.668 |
| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.159 | 2.099 |
| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.168 | 2.837 |
| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 5.287 | 3.636 |
| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 3.452 | 2.749 |
| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.705 | 4.688 |
| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 7.089 | 5.075 |
| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 3.43 | 2.492 |
| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 2.338 | 2.099 |
| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 1.96 | 1.811 |
| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 1.882 | 1.9 |
| gsarti/flores_101_est | est | byte_perplexity ↓ | 5.774 | 3.533 |
| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 2.431 | 2.444 |
| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 4.304 | 2.601 |
| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.937 | 1.984 |
| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 9.74 | 11.84 |
| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 6.035 | 3.914 |
| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 2.365 | 3.015 |
| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 5.707 | 2.438 |
| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 8.855 | 5.283 |
| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 2.921 | 2.903 |
| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 5.452 | 1.86 |
| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 3.706 | 2.715 |
| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 4.059 | 2.865 |
| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.127 | 3.411 |
| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 3.95 | 8.008 |
| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 1.976 | 2.632 |
| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 5.501 | 4.701 |
| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.314 | 2.104 |
| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 4.942 | 8.16 |
| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.259 | 2.198 |
| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 9.743 | 10.981 |
| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 6.234 | 2.373 |
| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.051 | 2.466 |
| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.039 | 4.376 |
| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 7.147 | 9.632 |
| gsarti/flores_101_khm | khm | byte_perplexity ↓ | 3.367 | 2.646 |
| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.241 | 4.522 |
| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 2.902 | 3.376 |
| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.331 | 3.106 |
| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 5.224 | 4.811 |
| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 4.847 | 8.871 |
| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 4.543 | 5.183 |
| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 5.591 | 7.158 |
| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 5.43 | 7.399 |
| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 12.031 | 11.951 |
| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.794 | 2.054 |
| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 6.857 | 2.274 |
| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.335 | 2.538 |
| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 9.041 | 5.996 |
| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.095 | 4.519 |
| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 5.266 | 4.438 |
| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.222 | 2.935 |
| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.523 | 2.413 |
| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 2.799 | 2.293 |
| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 3.629 | 2.593 |
| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 6.666 | 2.499 |
| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 5.015 | 8.485 |
| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 4.938 | 7.548 |
| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 3.607 | 4.936 |
| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 11.316 | 7.145 |
| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.982 | 2.668 |
| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.772 | 2.782 |
| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 3.012 | 2.432 |
| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.841 | 2.178 |
| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.624 | 4.785 |
| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 3.05 | 2.197 |
| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 1.708 | 1.689 |
| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 4.038 | 3.419 |
| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 4.141 | 3.582 |
| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 4.711 | 5.588 |
| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 4.206 | 5.667 |
| gsarti/flores_101_som | som | byte_perplexity ↓ | 9.154 | 4.788 |
| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.796 | 2.098 |
| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.241 | 2.688 |
| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 3.345 | 2.468 |
| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 2.684 | 4.473 |
| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 5.165 | 2.024 |
| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 6.81 | 2.407 |
| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.785 | 4.899 |
| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 3.75 | 2.738 |
| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.104 | 2.035 |
| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 3.318 | 2.622 |
| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.089 | 1.93 |
| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 11.766 | 11.64 |
| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.779 | 2.982 |
| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 8.5 | 13.209 |
| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.659 | 2.229 |
| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 6.142 | 13.945 |
| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 4.69 | 8.42 |
| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 4.361 | 7.636 |
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.118 | 5.113 |
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.274 | 5.67 |
| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 6.017 | 7.341 |
| headqa | esp | acc ↑ | 0.346 | 0.244 |
| hellaswag | eng | acc ↑ | 0.535 | 0.592 |
| lambada_mt_de | deu | acc ↑ | 0.329 | 0.358 |
| lambada_mt_en | eng | acc ↑ | 0.672 | 0.747 |
| lambada_mt_es | esp | acc ↑ | 0.476 | 0.397 |
| lambada_mt_it | ita | acc ↑ | 0.406 | 0.409 |
| logiqa | eng | acc ↑ | 0.235 | 0.244 |
| mathqa | eng | acc ↑ | 0.277 | 0.268 |
| mc_taco | eng | em ↑ | 0.131 | 0.124 |
| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.355 | 0.36 |
| mrpc | eng | acc ↑ | 0.387 | 0.446 |
| multirc (Median of 11 prompts) | eng | acc ↑ | 0.571 | 0.599 |
| openbookqa | eng | acc ↑ | 0.312 | 0.322 |
| piqa | eng | acc ↑ | 0.781 | 0.791 |
| prost | eng | acc ↑ | 0.298 | 0.299 |
| pubmedqa | eng | acc ↑ | 0.741 | 0.709 |
| qnli | eng | acc ↑ | 0.517 | 0.554 |
| qqp (Median of 7 prompts) | eng | acc ↑ | 0.588 | 0.395 |
| race | eng | acc ↑ | 0.39 | 0.402 |
| rte (Median of 6 prompts) | eng | acc ↑ | 0.52 | 0.495 |
| sciq | eng | acc ↑ | 0.936 | 0.948 |
| sst (Median of 6 prompts) | eng | acc ↑ | 0.604 | 0.647 |
| triviaqa | eng | acc ↑ | 0.183 | 0.342 |
| tydiqa_primary (Median of 16 prompts) | eng | acc ↑ | 0.281 | 0.148 |
| webqs | eng | acc ↑ | 0.062 | 0.159 |
| wic (Median of 11 prompts) | eng | acc ↑ | 0.506 | 0.498 |
| winogrande | eng | acc ↑ | 0.71 | 0.736 |
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.57 | 0.563 |
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.519 | 0.413 |
| humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
| humaneval | python | pass@10 ↑ | 0.322 | 0.0 |
| humaneval | python | pass@100 ↑ | 0.555 | 0.003 |
**Train-time Evaluation:**
Final checkpoint after 95K steps:
- Training Loss: 1.939
- Validation Loss: 2.061
- Perplexity: 7.045
For more see: https://huggingface.co/bigscience/tr11-176B-ml-logs
</details>
---
# Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary>
- 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 trained or finetuned downstream of BLOOM LM 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>
---
# Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary>
- <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.
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# More Information
*This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.*
<details>
<summary>Click to expand</summary>
## Intermediate checkpoints
For academic (or any) usage, we published the intermediate checkpoints, corresponding to the model state at each 5000 steps. Please follow [this link](https://huggingface.co/bigscience/bloom-176-intermediate) to get these checkpoints.
## 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
## Lessons
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
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# 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