readme: ---
  datasets:
  - bigscience/xP3mt
  - mc4
  license: apache-2.0
  language:
  - af
  - am
  - ar
  - az
  - be
  - bg
  - bn
  - ca
  - ceb
  - co
  - cs
  - cy
  - da
  - de
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fil
  - fr
  - fy
  - ga
  - gd
  - gl
  - gu
  - ha
  - haw
  - hi
  - hmn
  - ht
  - hu
  - hy
  - ig
  - is
  - it
  - iw
  - ja
  - jv
  - ka
  - kk
  - km
  - kn
  - ko
  - ku
  - ky
  - la
  - lb
  - lo
  - lt
  - lv
  - mg
  - mi
  - mk
  - ml
  - mn
  - mr
  - ms
  - mt
  - my
  - ne
  - nl
  - no
  - ny
  - pa
  - pl
  - ps
  - pt
  - ro
  - ru
  - sd
  - si
  - sk
  - sl
  - sm
  - sn
  - so
  - sq
  - sr
  - st
  - su
  - sv
  - sw
  - ta
  - te
  - tg
  - th
  - tr
  - uk
  - und
  - ur
  - uz
  - vi
  - xh
  - yi
  - yo
  - zh
  - zu
  tags:
  - text2text-generation
  widget:
  - text: "Life is beautiful! Translate to Mongolian."
    example_title: "mn-en translation"
  - text: "Le mot japonais «憂鬱» veut dire quoi en Odia?"
    example_title: "jp-or-fr translation"
  - text: "Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte stell die Frage auf Norwegisch."
    example_title: "de-nb quiz"
  - text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?"
    example_title: "zh-en sentiment"
  - text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？"
    example_title: "zh-zh sentiment"
  - text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"."
    example_title: "vi-en query"
  - text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»."
    example_title: "fr-fr query"
  - text: "Explain in a sentence in Telugu what is backpropagation in neural networks."
    example_title: "te-en qa"
  - text: "Why is the sky blue?"
    example_title: "en-en qa"
  - text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):"
    example_title: "es-en fable"
  - text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):"
    example_title: "hi-en fable"
  model-index:
  - name: mt0-xxl-mt
    results:
    - task:
        type: Coreference resolution
      dataset:
        type: winogrande
        name: Winogrande XL (xl)
        config: xl
        split: validation
        revision: a80f460359d1e9a67c006011c94de42a8759430c
      metrics:
      - type: Accuracy
        value: 62.67
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (en)
        config: en
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 83.31
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (fr)
        config: fr
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 78.31
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (jp)
        config: jp
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 80.19
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (pt)
        config: pt
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 80.99
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (ru)
        config: ru
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 79.05
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (zh)
        config: zh
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 82.34
    - task:
        type: Natural language inference
      dataset:
        type: anli
        name: ANLI (r1)
        config: r1
        split: validation
        revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
      metrics:
      - type: Accuracy
        value: 49.5
    - task:
        type: Natural language inference
      dataset:
        type: anli
        name: ANLI (r2)
        config: r2
        split: validation
        revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
      metrics:
      - type: Accuracy
        value: 42.0
    - task:
        type: Natural language inference
      dataset:
        type: anli
        name: ANLI (r3)
        config: r3
        split: validation
        revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
      metrics:
      - type: Accuracy
        value: 48.17
    - task:
        type: Natural language inference
      dataset:
        type: super_glue
        name: SuperGLUE (cb)
        config: cb
        split: validation
        revision: 9e12063561e7e6c79099fe