README.md

metadata

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

BLOOM LM

BigScience Large Open-science Open-access Multilingual Language Model

Model Card

Version 1.0 / 26.May.2022

Table of Contents

1. Model Details
2. Uses
3. Training Data
4. Risks and Limitations
5. Evaluation
6. Recommendations
7. Glossary and Calculations
8. More Information
9. Model Card Authors

Model Details

Basics

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

Click to expand

Developed by: BigScience (website)

• 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

License: RAIL License v1.0 (link)

Release Date Estimate: Monday, 11.July.2022

Send Questions to: [email protected]

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).

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

Technical Specifications

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

Click to expand

Please see the BLOOM training README for full details on replicating training.

Model Architecture: Modified from Megatron-LM GPT2 (see paper, BLOOM Megatron code):

• Decoder-only architecture

• Layer normalization applied to word embeddings layer (StableEmbedding; see code, paper)

• ALiBI positional encodings (see paper), 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, tokenizer description)

Objective Function: Cross Entropy with mean reduction (see API documentation).

Compute infrastructure: Jean Zay Public Supercomputer, provided by the French government (see announcement).

• 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)

  • DeepSpeed (Github link)

  • PyTorch (pytorch-1.11 w/ CUDA-11.5; see Github link)

  • apex (Github link)

Training

Training logs: Tensorboard link

• 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) 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.

Environmental Impact

Click to expand

The training supercomputer, Jean Zay (website), 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.)

 

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.

Click to expand

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, 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 settings is out of scope for this model.  The model is not designed for 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, 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

• Unconsented impersonation and imitation

• Unconsented surveillance

• Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions

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

• Users of Derivatives of the Model, as described in the 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

 

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.

Click to expand

Details for each dataset are provided in individual Data Cards.

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 for more.)

Languages

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

The following table shows the further distribution of Niger-Congo and Indic languages in the training data.

Click to expand

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

The following table shows the distribution of programming languages.

Click to expand

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

 

Risks and Limitations

This section identifies foreseeable harms and misunderstandings.

Click to expand

Model may:

• Overrepresent some viewpoints and underrepresent others

• Contain stereotypes

• Contain personal 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

 

Evaluation

This section describes the evaluation protocols and provides the results.

Click to expand

Metrics

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

Includes:

Metric	Why chosen
Perplexity	Standard metric for quantifying model improvements during training
Cross Entropy 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 and 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

 

Recommendations

This section provides information on warnings and potential mitigations.

Click to expand

• 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, 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.

 

Glossary and Calculations

This section defines common terms and how metrics are calculated.

Click to expand

• Loss: 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.

• Perplexity: 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.

• High-stakes settings: Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed Artificial Intelligence (AI) Act.

• Critical decisions: Such as those defined in the United States' proposed Algorithmic Accountability Act.

• Human rights: Includes those rights defined in the Universal Declaration of Human Rights.

• Personal Data and Personal Information: Personal data and information is defined in multiple data protection regulations, such as "personal data" in the European Union's General Data Protection Regulation; and "personal information" in the Republic of South Africa's Protection of Personal Information Act, The People's Republic of China's Personal information protection law.

• Sensitive characteristics: This includes specifically protected categories in human rights (see UHDR, Article 2) and personal information regulation (see GDPR, Article 9; Protection of Personal Information Act, Chapter 1)

• Deception: 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.

 

More Information

Click to expand

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

 

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	31.5
ARC (25-shot)	35.75
HellaSwag (10-shot)	54.37
MMLU (5-shot)	26.59
TruthfulQA (0-shot)	40.57
Winogrande (5-shot)	57.62
GSM8K (5-shot)	0.83
DROP (3-shot)	4.74