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README.md CHANGED
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1
  ---
2
- license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ datasets:
3
+ - bigscience/xP3mt
4
+ license: bigscience-bloom-rail-1.0
5
+ language:
6
+ - ak
7
+ - ar
8
+ - as
9
+ - bm
10
+ - bn
11
+ - ca
12
+ - code
13
+ - en
14
+ - es
15
+ - eu
16
+ - fon
17
+ - fr
18
+ - gu
19
+ - hi
20
+ - id
21
+ - ig
22
+ - ki
23
+ - kn
24
+ - lg
25
+ - ln
26
+ - ml
27
+ - mr
28
+ - ne
29
+ - nso
30
+ - ny
31
+ - or
32
+ - pa
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+ - pt
34
+ - rn
35
+ - rw
36
+ - sn
37
+ - st
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+ - sw
39
+ - ta
40
+ - te
41
+ - tn
42
+ - ts
43
+ - tum
44
+ - tw
45
+ - ur
46
+ - vi
47
+ - wo
48
+ - xh
49
+ - yo
50
+ - zh
51
+ - zu
52
+ programming_language:
53
+ - C
54
+ - C++
55
+ - C#
56
+ - Go
57
+ - Java
58
+ - JavaScript
59
+ - Lua
60
+ - PHP
61
+ - Python
62
+ - Ruby
63
+ - Rust
64
+ - Scala
65
+ - TypeScript
66
+ pipeline_tag: text-generation
67
+ widget:
68
+ - 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?"
69
+ example_title: "zh-en sentiment"
70
+ - 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?"
71
+ example_title: "zh-zh sentiment"
72
+ - text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"."
73
+ example_title: "vi-en query"
74
+ - text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»."
75
+ example_title: "fr-fr query"
76
+ - text: "Explain in a sentence in Telugu what is backpropagation in neural networks."
77
+ example_title: "te-en qa"
78
+ - text: "Why is the sky blue?"
79
+ example_title: "en-en qa"
80
+ - 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):"
81
+ example_title: "es-en fable"
82
+ - 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):"
83
+ example_title: "hi-en fable"
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+ model-index:
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+ - name: bloomz-7b1-mt
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+ results:
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+ - task:
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+ type: Coreference resolution
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+ dataset:
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+ type: winogrande
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+ name: Winogrande XL (xl)
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+ config: xl
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+ split: validation
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+ revision: a80f460359d1e9a67c006011c94de42a8759430c
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+ metrics:
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+ - type: Accuracy
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+ value: 56.51
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+ - task:
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+ type: Coreference resolution
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+ dataset:
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+ type: Muennighoff/xwinograd
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+ name: XWinograd (en)
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+ config: en
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+ split: test
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+ revision: 9dd5ea5505fad86b7bedad667955577815300cee
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+ metrics:
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+ - type: Accuracy
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+ value: 65.76
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+ - task:
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+ type: Coreference resolution
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+ dataset:
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+ type: Muennighoff/xwinograd
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+ name: XWinograd (fr)
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+ config: fr
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+ split: test
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+ revision: 9dd5ea5505fad86b7bedad667955577815300cee
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+ metrics:
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+ - type: Accuracy
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+ value: 57.83
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+ - task:
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+ type: Coreference resolution
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+ dataset:
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+ type: Muennighoff/xwinograd
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+ name: XWinograd (jp)
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+ config: jp
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+ split: test
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+ revision: 9dd5ea5505fad86b7bedad667955577815300cee
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+ metrics:
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+ - type: Accuracy
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+ value: 51.82
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+ - task:
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+ type: Coreference resolution
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+ dataset:
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+ type: Muennighoff/xwinograd
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+ name: XWinograd (pt)
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+ config: pt
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+ split: test
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+ revision: 9dd5ea5505fad86b7bedad667955577815300cee
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+ metrics:
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+ - type: Accuracy
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+ value: 57.41
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+ - task:
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+ type: Coreference resolution
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+ dataset:
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+ type: Muennighoff/xwinograd
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+ name: XWinograd (ru)
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+ config: ru
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+ split: test
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+ revision: 9dd5ea5505fad86b7bedad667955577815300cee
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+ metrics:
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+ - type: Accuracy
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+ value: 55.87
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+ - task:
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+ type: Coreference resolution
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+ dataset:
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+ type: Muennighoff/xwinograd
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+ name: XWinograd (zh)
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+ config: zh
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+ split: test
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+ revision: 9dd5ea5505fad86b7bedad667955577815300cee
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+ metrics:
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+ - type: Accuracy
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+ value: 62.7
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: anli
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+ name: ANLI (r1)
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+ config: r1
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+ split: validation
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+ revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
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+ metrics:
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+ - type: Accuracy
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+ value: 42.6
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: anli
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+ name: ANLI (r2)
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+ config: r2
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+ split: validation
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+ revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
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+ metrics:
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+ - type: Accuracy
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+ value: 39.4
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: anli
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+ name: ANLI (r3)
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+ config: r3
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+ split: validation
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+ revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
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+ metrics:
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+ - type: Accuracy
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+ value: 42.0
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: super_glue
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+ name: SuperGLUE (cb)
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+ config: cb
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+ split: validation
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+ revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
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+ metrics:
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+ - type: Accuracy
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+ value: 83.93
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: super_glue
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+ name: SuperGLUE (rte)
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+ config: rte
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+ split: validation
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+ revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
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+ metrics:
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+ - type: Accuracy
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+ value: 82.67
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (ar)
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+ config: ar
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 55.58
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (bg)
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+ config: bg
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 44.9
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (de)
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+ config: de
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 48.92
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (el)
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+ config: el
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 42.89
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (en)
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+ config: en
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 58.92
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (es)
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+ config: es
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 57.35
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+ - task:
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+ type: Natural language inference
287
+ dataset:
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+ type: xnli
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+ name: XNLI (fr)
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+ config: fr
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
293
+ metrics:
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+ - type: Accuracy
295
+ value: 56.67
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+ - task:
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+ type: Natural language inference
298
+ dataset:
299
+ type: xnli
300
+ name: XNLI (hi)
301
+ config: hi
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 53.45
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+ - task:
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+ type: Natural language inference
309
+ dataset:
310
+ type: xnli
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+ name: XNLI (ru)
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+ config: ru
313
+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 50.24
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (sw)
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+ config: sw
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 48.27
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+ - task:
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+ type: Natural language inference
331
+ dataset:
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+ type: xnli
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+ name: XNLI (th)
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+ config: th
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 41.08
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+ - task:
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+ type: Natural language inference
342
+ dataset:
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+ type: xnli
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+ name: XNLI (tr)
345
+ config: tr
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 38.71
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (ur)
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+ config: ur
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 49.48
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (vi)
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+ config: vi
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
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+ value: 54.5
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+ - task:
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+ type: Natural language inference
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+ dataset:
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+ type: xnli
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+ name: XNLI (zh)
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+ config: zh
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+ split: validation
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+ revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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+ metrics:
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+ - type: Accuracy
383
+ value: 54.3
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+ - task:
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+ type: Program synthesis
386
+ dataset:
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+ type: openai_humaneval
388
+ name: HumanEval
389
+ config: None
390
+ split: test
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+ revision: e8dc562f5de170c54b5481011dd9f4fa04845771
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+ metrics:
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+ - type: Pass@1
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+ value: 7.23
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+ - type: Pass@10
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+ value: 14.46
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+ - type: Pass@100
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+ value: 25.86
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: story_cloze
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+ name: StoryCloze (2016)
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+ config: "2016"
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+ split: validation
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+ revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
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+ metrics:
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+ - type: Accuracy
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+ value: 89.58
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: super_glue
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+ name: SuperGLUE (copa)
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+ config: copa
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+ split: validation
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+ revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
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+ metrics:
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+ - type: Accuracy
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+ value: 84.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (et)
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+ config: et
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 52.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (ht)
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+ config: ht
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 54.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (id)
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+ config: id
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (it)
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+ config: it
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 62.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (qu)
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+ config: qu
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 61.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (sw)
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+ config: sw
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 61.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (ta)
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+ config: ta
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 62.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (th)
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+ config: th
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 61.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (tr)
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+ config: tr
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 56.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (vi)
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+ config: vi
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 77.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: xcopa
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+ name: XCOPA (zh)
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+ config: zh
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+ split: validation
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+ revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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+ metrics:
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+ - type: Accuracy
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+ value: 80.0
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: Muennighoff/xstory_cloze
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+ name: XStoryCloze (ar)
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+ config: ar
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+ split: validation
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+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
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+ metrics:
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+ - type: Accuracy
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+ value: 83.85
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+ - task:
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+ type: Sentence completion
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+ dataset:
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+ type: Muennighoff/xstory_cloze
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+ name: XStoryCloze (es)
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+ config: es
559
+ split: validation
560
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
561
+ metrics:
562
+ - type: Accuracy
563
+ value: 88.82
564
+ - task:
565
+ type: Sentence completion
566
+ dataset:
567
+ type: Muennighoff/xstory_cloze
568
+ name: XStoryCloze (eu)
569
+ config: eu
570
+ split: validation
571
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
572
+ metrics:
573
+ - type: Accuracy
574
+ value: 73.26
575
+ - task:
576
+ type: Sentence completion
577
+ dataset:
578
+ type: Muennighoff/xstory_cloze
579
+ name: XStoryCloze (hi)
580
+ config: hi
581
+ split: validation
582
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
583
+ metrics:
584
+ - type: Accuracy
585
+ value: 80.41
586
+ - task:
587
+ type: Sentence completion
588
+ dataset:
589
+ type: Muennighoff/xstory_cloze
590
+ name: XStoryCloze (id)
591
+ config: id
592
+ split: validation
593
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
594
+ metrics:
595
+ - type: Accuracy
596
+ value: 84.58
597
+ - task:
598
+ type: Sentence completion
599
+ dataset:
600
+ type: Muennighoff/xstory_cloze
601
+ name: XStoryCloze (my)
602
+ config: my
603
+ split: validation
604
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
605
+ metrics:
606
+ - type: Accuracy
607
+ value: 51.56
608
+ - task:
609
+ type: Sentence completion
610
+ dataset:
611
+ type: Muennighoff/xstory_cloze
612
+ name: XStoryCloze (ru)
613
+ config: ru
614
+ split: validation
615
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
616
+ metrics:
617
+ - type: Accuracy
618
+ value: 64.26
619
+ - task:
620
+ type: Sentence completion
621
+ dataset:
622
+ type: Muennighoff/xstory_cloze
623
+ name: XStoryCloze (sw)
624
+ config: sw
625
+ split: validation
626
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
627
+ metrics:
628
+ - type: Accuracy
629
+ value: 71.01
630
+ - task:
631
+ type: Sentence completion
632
+ dataset:
633
+ type: Muennighoff/xstory_cloze
634
+ name: XStoryCloze (te)
635
+ config: te
636
+ split: validation
637
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
638
+ metrics:
639
+ - type: Accuracy
640
+ value: 73.06
641
+ - task:
642
+ type: Sentence completion
643
+ dataset:
644
+ type: Muennighoff/xstory_cloze
645
+ name: XStoryCloze (zh)
646
+ config: zh
647
+ split: validation
648
+ revision: 8bb76e594b68147f1a430e86829d07189622b90d
649
+ metrics:
650
+ - type: Accuracy
651
+ value: 85.9
652
  ---
653
+
654
+ ![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true)
655
+
656
+ # Table of Contents
657
+
658
+ 1. [Model Summary](#model-summary)
659
+ 2. [Use](#use)
660
+ 3. [Limitations](#limitations)
661
+ 4. [Training](#training)
662
+ 5. [Evaluation](#evaluation)
663
+ 7. [Citation](#citation)
664
+
665
+ # Model Summary
666
+
667
+ > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
668
+
669
+ - **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
670
+ - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
671
+ - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
672
+ - **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
673
+ - **BLOOMZ & mT0 Model Family:**
674
+
675
+ <div class="max-w-full overflow-auto">
676
+ <table>
677
+ <tr>
678
+ <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
679
+ </tr>
680
+ <tr>
681
+ <td>Parameters</td>
682
+ <td>300M</td>
683
+ <td>580M</td>
684
+ <td>1.2B</td>
685
+ <td>3.7B</td>
686
+ <td>13B</td>
687
+ <td>560M</td>
688
+ <td>1.1B</td>
689
+ <td>1.7B</td>
690
+ <td>3B</td>
691
+ <td>7.1B</td>
692
+ <td>176B</td>
693
+ </tr>
694
+ <tr>
695
+ <td>Finetuned Model</td>
696
+ <td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
697
+ <td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
698
+ <td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
699
+ <td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
700
+ <td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
701
+ <td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
702
+ <td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
703
+ <td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
704
+ <td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
705
+ <td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
706
+ <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
707
+ </tr>
708
+ </tr>
709
+ <tr>
710
+ <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
711
+ </tr>
712
+ <tr>
713
+ <td>Finetuned Model</td>
714
+ <td></td>
715
+ <td></td>
716
+ <td></td>
717
+ <td></td>
718
+ <td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
719
+ <td></td>
720
+ <td></td>
721
+ <td></td>
722
+ <td></td>
723
+ <td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
724
+ <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
725
+ </tr>
726
+ <th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
727
+ </tr>
728
+ <tr>
729
+ <td>Finetuned Model</td>
730
+ <td></td>
731
+ <td></td>
732
+ <td></td>
733
+ <td></td>
734
+ <td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
735
+ <td></td>
736
+ <td></td>
737
+ <td></td>
738
+ <td></td>
739
+ <td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
740
+ <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
741
+ </tr>
742
+ <th colspan="12">Original pretrained checkpoints. Not recommended.</th>
743
+ <tr>
744
+ <td>Pretrained Model</td>
745
+ <td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
746
+ <td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
747
+ <td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
748
+ <td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
749
+ <td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
750
+ <td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
751
+ <td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
752
+ <td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
753
+ <td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
754
+ <td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
755
+ <td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
756
+ </tr>
757
+ </table>
758
+ </div>
759
+
760
+
761
+ # Use
762
+
763
+ ## Intended use
764
+
765
+ We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper:
766
+ - 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?
767
+ - Suggest at least five related search terms to "Mạng neural nhân tạo".
768
+ - 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):
769
+ - Explain in a sentence in Telugu what is backpropagation in neural networks.
770
+
771
+ **Feel free to share your generations in the Community tab!**
772
+
773
+ ## How to use
774
+
775
+ ### CPU
776
+
777
+ <details>
778
+ <summary> Click to expand </summary>
779
+
780
+ ```python
781
+ # pip install -q transformers
782
+ from transformers import AutoModelForCausalLM, AutoTokenizer
783
+
784
+ checkpoint = "bigscience/bloomz-7b1-mt"
785
+
786
+ tokenizer = AutoTokenizer.from_pretrained(checkpoint)
787
+ model = AutoModelForCausalLM.from_pretrained(checkpoint)
788
+
789
+ inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
790
+ outputs = model.generate(inputs)
791
+ print(tokenizer.decode(outputs[0]))
792
+ ```
793
+
794
+ </details>
795
+
796
+ ### GPU
797
+
798
+ <details>
799
+ <summary> Click to expand </summary>
800
+
801
+ ```python
802
+ # pip install -q transformers accelerate
803
+ from transformers import AutoModelForCausalLM, AutoTokenizer
804
+
805
+ checkpoint = "bigscience/bloomz-7b1-mt"
806
+
807
+ tokenizer = AutoTokenizer.from_pretrained(checkpoint)
808
+ model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")
809
+
810
+ inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
811
+ outputs = model.generate(inputs)
812
+ print(tokenizer.decode(outputs[0]))
813
+ ```
814
+
815
+ </details>
816
+
817
+ ### GPU in 8bit
818
+
819
+ <details>
820
+ <summary> Click to expand </summary>
821
+
822
+ ```python
823
+ # pip install -q transformers accelerate bitsandbytes
824
+ from transformers import AutoModelForCausalLM, AutoTokenizer
825
+
826
+ checkpoint = "bigscience/bloomz-7b1-mt"
827
+
828
+ tokenizer = AutoTokenizer.from_pretrained(checkpoint)
829
+ model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)
830
+
831
+ inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
832
+ outputs = model.generate(inputs)
833
+ print(tokenizer.decode(outputs[0]))
834
+ ```
835
+
836
+ </details>
837
+
838
+ <!-- Necessary for whitespace -->
839
+ ###
840
+
841
+ # Limitations
842
+
843
+ **Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".
844
+
845
+ # Training
846
+
847
+ ## Model
848
+
849
+ - **Architecture:** Same as [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1), also refer to the `config.json` file
850
+ - **Finetuning steps:** 1000
851
+ - **Finetuning tokens:** 4.19 billion
852
+ - **Finetuning layout:** 1x pipeline parallel, 1x tensor parallel, 64x data parallel
853
+ - **Precision:** float16
854
+
855
+ ## Hardware
856
+
857
+ - **CPUs:** AMD CPUs with 512GB memory per node
858
+ - **GPUs:** 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links
859
+ - **Communication:** NCCL-communications network with a fully dedicated subnet
860
+
861
+ ## Software
862
+
863
+ - **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
864
+ - **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed)
865
+ - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5)
866
+ - **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
867
+
868
+ # Evaluation
869
+
870
+ We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
871
+
872
+ # Citation
873
+ ```bibtex
874
+ @article{muennighoff2022crosslingual,
875
+ title={Crosslingual generalization through multitask finetuning},
876
+ author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
877
+ journal={arXiv preprint arXiv:2211.01786},
878
+ year={2022}
879
+ }
880
+ ```
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