Model: bigscience/mt0-xxl
Author: bigscience
Pipeline: text-generation
Library: transformers
Tags: transformers, pytorch, safetensors, mt5, text2text-generation, 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, dataset:bigscience/xP3, dataset:mc4, arxiv:2211.01786, license:apache-2.0, model-index, endpoints_compatible, region:us
Card metadata: datasets: bigscience/xP3, 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: <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>  <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>  <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>  <td>xP3 + our evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>  <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>  <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> Which dataset has the most tasks?
example_title: en-en struct-to-text, 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: 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 our resulting models capable of crosslingual generalization to unseen tasks & languages. What are the keywords in Chinese?
example_title: zh-en keywords, 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
results: task: type: Coreference resolution
dataset: type: winogrande
name: Winogrande XL (xl)
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics: type: Accuracy
value: 63.38
verified: no, task: type: Coreference resolution
dataset: type: Muennighoff/xwinograd
name: XWinograd (en)
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics: type: Accuracy
value: 81.29
verified: no, task: type: Coreference resolution
dataset: type: Muennighoff/xwinograd
name: XWinograd (fr)
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics: type: Accuracy
value: 78.31
verified: no, task: type: Coreference resolution
dataset: type: Muennighoff/xwinograd
name: XWinograd (jp)
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics: type: Accuracy
value: 78.62
verified: no, task: type: Coreference resolution
dataset: type: Muennighoff/xwinograd
name: XWinograd (pt)
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics: type: Accuracy
value: 77.95
verified: no, task: type: Coreference resolution
dataset: type: Muennighoff/xwinograd
name: XWinograd (ru)
config: ru
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics: type: Accuracy
value: 76.51
verified: no, task: type: Coreference resolution
dataset: type: Muennighoff/xwinograd
name: XWinograd (zh)
config: zh
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics: type: Accuracy
value: 77.38
verified: no, task: type: Natural language inference
dataset: type: anli
name: ANLI (r1)
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics: type: Accuracy
value: 49.5
verified: no, task: type: Natural language inference
dataset: type: anli
name: ANLI (r2)
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics: type: Accuracy
value: 43
verified: no, task: type: Natural language inference
dataset: type: anli
name: ANLI (r3)
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics: type: Accuracy
value: 46.08
verified: no, task: type: Natural language inference
dataset: type: super_glue
name: SuperGLUE (cb)
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics: type: Accuracy
value: 85.71
verified: no, task: type: Natural language inference
dataset: type: super_glue
name: SuperGLUE (rte)
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics: type: Accuracy
value: 85.56
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (ar)
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 57.91
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (bg)
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 59.88
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (de)
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 60.64
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (el)
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 59
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (en)
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 62.01
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (es)
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 60.8
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (fr)
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 59.88
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (hi)
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 57.23
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (ru)
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 58.88
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (sw)
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 55.66
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (th)
config: th
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 57.43
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (tr)
config: tr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 57.59
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (ur)
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 55.42
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (vi)
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 58.51
verified: no, task: type: Natural language inference
dataset: type: xnli
name: XNLI (zh)
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics: type: Accuracy
value: 59.12
verified: no, task: type: Sentence completion
dataset: type: story_cloze
name: StoryCloze (2016)
config: 2016
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics: type: Accuracy
value: 96.04
verified: no, task: type: Sentence completion
dataset: type: super_glue
name: SuperGLUE (copa)
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics: type: Accuracy
value: 93
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (et)
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 79
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (ht)
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 81
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (id)
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 92
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (it)
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 90
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (qu)
config: qu
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 59
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (sw)
config: sw
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 79
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (ta)
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 84
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (th)
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 77
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (tr)
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 79
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (vi)
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 88
verified: no, task: type: Sentence completion
dataset: type: xcopa
name: XCOPA (zh)
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics: type: Accuracy
value: 89
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (ar)
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 91.07
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (es)
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 92.52
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (eu)
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 90.6
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (hi)
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 92.32
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (id)
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 93.51
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (my)
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 87.49
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (ru)
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 91.4
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (sw)
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 89.41
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (te)
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 90.54
verified: no, task: type: Sentence completion
dataset: type: Muennighoff/xstory_cloze
name: XStoryCloze (zh)
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics: type: Accuracy
value: 93.85
verified: no
pipeline_tag: text2text-generation
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Created: 2022-10-19 23:12:28