readme: ---
  datasets:
  - bigscience/xP3
  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
  - zu
  programming_language: 
  - C
  - C++
  - C#
  - Go
  - Java
  - JavaScript
  - Lua
  - PHP
  - Python
  - Ruby
  - Rust
  - Scala
  - TypeScript
  pipeline_tag: text-generation
  widget:
  - 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: bloomz-1b7
    results:
    - task:
        type: Coreference resolution
      dataset:
        type: winogrande
        name: Winogrande XL (xl)
        config: xl
        split: validation
        revision: a80f460359d1e9a67c006011c94de42a8759430c
      metrics:
      - type: Accuracy
        value: 51.14
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (en)
        config: en
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 56.34
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (fr)
        config: fr
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 55.42
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (jp)
        config: jp
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 52.55
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (pt)
        config: pt
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 53.23
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (ru)
        config: ru
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 55.24
    - task:
        type: Coreference resolution
      dataset:
        type: Muennighoff/xwinograd
        name: XWinograd (zh)
        config: zh
        split: test
        revision: 9dd5ea5505fad86b7bedad667955577815300cee
      metrics:
      - type: Accuracy
        value: 56.15
    - task:
        type: Natural language inference
      dataset:
        type: anli
        name: ANLI (r1)
        config: r1
        split: validation
        revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
      metrics:
      - type: Accuracy
        value: 34.0
    - task:
        type: Natural language inference
      dataset:
        type: anli
        name: ANLI (r2)
        config: r2
        split: validation
        revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
      metrics:
      - type: Accuracy
        value: 36.1
    - task:
        type: Natural language inference
      dataset:
        type: anli
        name: ANLI (r3)
        config: r3
        split: validation
        revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
      metrics:
      - type: Accuracy
        value: 37.08
    - task:
        type: Natural language inference
      dataset:
        type: super_glue
        name: SuperGLUE (cb)
        config: cb
        split: validation
        revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
      metrics:
      - type: Accuracy
        value: 71.43
    - task:
        type: Natural language inference
      dataset:
        type: super_glue
        name: SuperGLUE (rte)
        config: rte
        split: validation
        revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
      metrics:
      - type: Accuracy
        value: 76.17
    - task:
        type: Natural language inference
      dataset:
        type: xnli
        name: XNLI (ar)
        config: ar
        split: validation
  